Pith. sign in

REVIEW 3 major objections 4 minor 112 references

Image Restoration via Multi-domain Learning

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read One network handles ten image-restoration tasks by mixing spatial, wavelet, and Fourier features.

desk verdict SWFormer is a genuinely efficient and broadly tested restoration backbone, but the SOTA claim only holds for the largest variant and the 'lossless' wavelet claim needs proof before the paper is publishable as written. read the letter →

arxiv 2505.05504 v1 pith:C5JI4KB3 submitted 2025-05-07 eess.IV cs.CV

Xingyu Jiang , Ning Gao , Xiuhui Zhang , Hongkun Dou , Shaowen Fu , Xiaoqing Zhong , Hongjue Li , Yue Deng This is my paper · ORCID
classification eess.IVcs.CV
keywords imagerestorationmulti-domainlearningwavelettransformFouriertransformerderainingdehazinglow-lightenhancement
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a single Transformer-style backbone, SWFormer, can outperform task-specific state-of-the-art models across ten different image restoration tasks by exploiting a prior shared by all degradations: in Fourier space and in wavelet sub-bands, the difference between a degraded image and a clean one is concentrated rather than spread out. It supports this with qualitative swap experiments and with a redesigned token mixer that replaces self-attention with spatial, wavelet, and Fourier branches, together with a multi-scale feed-forward network and a multi-input/multi-output encoder-decoder. The payoff, if correct, is practical: high restoration quality without the heavy training and inference cost that self-attention-based restoration models carry. The paper reports state-of-the-art results on most of twenty-six benchmarks while using a fraction of the parameters, FLOPs, and latency of comparable transformers.

What carries the argument

The central object is the Spatial-Wavelet-Fourier Mixer (SWFM), the token mixer that replaces vanilla self-attention. It splits an expanded feature into three branches: a depthwise convolution for local pixel-level features, a learnable two-dimensional wavelet transform followed by depthwise convolution and inverse wavelet transform for region-level sub-band features, and an FFT-based branch with a gating mechanism for global frequency filtering. The three branches are fused through channel attention, giving local-region-global receptive-field modeling at O(N log N) cost instead of the O($N^{2}$) cost of self-attention. The companion modules are the Multi-scale ConvFFN (MSFN), which aggregates multi-resolution features from those domains, and the Lossless Multi-Input Multi-Output (LMIMO) architecture, which feeds wavelet- or pixel-unshuffle-downsampled inputs and reconstructs outputs at several scales.

What would settle it

For each of the ten tasks, compute the fraction of degradation energy in each wavelet sub-band and Fourier band; if any task (for example, shadow removal or cloud removal) shows degradation spread roughly uniformly across sub-bands, the central prior is violated. A direct behavioral test is the paper's own swap experiment: swap the identified sub-bands between clear and degraded pairs and measure PSNR, and if for some task the swapped clear image is not close to the degraded image, the prior fails there.

Watch

Extended reading notes

Core claim

The paper's central claim is that looking at image degradation through a spatial-wavelet-Fourier lens reveals a common prior: in the Fourier domain, degradations such as rain, blur, haze, and low light are compact rather than spatially diffuse, and after a wavelet transform the degradation concentrates in specific sub-bands (for example, LL and HL for rain, LL for haze and low light). Swapping those sub-bands between a clear and a degraded image swaps the degradation itself, which the paper takes as evidence that the domain separation is a real structural property. On that basis SWFormer replaces the self-attention token mixer with a three-branch Spatial-Wavelet-Fourier Mixer that runs depthwise convolution, a learnable wavelet transform, and an FFT/IFFT gating branch in parallel, and replaces the usual feed-forward network with a multi-scale ConvFFN. Inside a lossless multi-input/multi-output encoder-decoder, the same trained model can produce small, medium, and large outputs. The paper reports that SWFormer reaches state-of-the-art PSNR/SSIM on most of twenty-six benchmarks across ten restoration tasks while using fewer parameters, fewer FLOPs, and lower latency than the transformers it compares against.

Load-bearing premise

The whole design assumes that across all ten degradations, the difference between a degraded image and a clear image stays concentrated in a few wavelet sub-bands and Fourier frequencies, so that processing those domains separately is enough; if that concentration fails for a given degradation, the multi-branch mixer could lose to simpler efficient backbones.

Editorial extensions

If this is right

  • Training a single SWFormer configuration on one task can serve as a strong shared encoder or initializer for other restoration tasks, because the multi-domain prior is treated as task-agnostic.
  • For deployment, SWFormer-s offers a low-latency option: at 256x256 resolution it restores Rain200L in 25.9 ms while beating the previous best deraining method in PSNR, and at 1024x1024 resolution it is much faster than DRSformer with higher PSNR.
  • The LMIMO design yields small, medium, and large variants from one training run, so a user can choose a compute budget without retraining separate models.
  • The Fourier branch's O(N log N) complexity makes the architecture suitable for high-resolution inputs where quadratic self-attention becomes prohibitive.
  • If the sub-band-separation prior holds, the same architectural recipe generalizes to degradations beyond the ten tested tasks, such as combining multiple degradations in one input image.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves the sub-band-separation claim qualitative; a direct next step is to quantify the fraction of degradation energy in each wavelet sub-band and Fourier band per task, and to verify that tasks with weaker separation lose less from removing that branch.
  • Because the wavelet branch uses learnable convolutions, one could test whether the learned filters converge to standard wavelet filters and whether the selected sub-bands shift across tasks, turning the prior into a measurable design rule.
  • The success across ten degradations suggests the multi-domain mixer could be transferred to neighboring low-level problems such as JPEG artifact removal or video restoration, but that extrapolation goes beyond the evidence in this paper.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The manuscript proposes SWFormer, a Transformer-like image-restoration backbone that replaces self-attention with a Spatial-Wavelet-Fourier Mixer (SWFM) and uses a Multi-scale ConvFFN (MSFN), all embedded in a Lossless Multi-Input Multi-Output (LMIMO) framework. The authors motivate the design with the qualitative observation that degradation energy concentrates in specific wavelet sub-bands and Fourier frequencies, allowing local-region-global receptive field modeling. They evaluate the method on 26 benchmarks across 10 restoration tasks, comparing three model variants (small, medium, large) against prior state-of-the-art methods in terms of PSNR/SSIM (and sometimes LPIPS), parameter count, FLOPs/MACs, and latency. The central claims are that SWFormer outperforms state-of-the-art methods on most datasets and achieves a favorable efficiency-performance trade-off.

Significance. If the claims hold, SWFormer-l would be a practical and reasonably efficient multi-task restoration backbone: it obtains the best or near-best PSNR on most of the 26 benchmarks, with a large reduction in parameters and FLOPs relative to several transformer baselines. The paper's strengths include the breadth of the experimental study, the inclusion of complexity and latency measurements, ablations of the main components, and a public code release. The performance margins over prior work are often small (0.1 to 0.4 dB), but they are consistent for the large variant on most datasets. A notable weakness is that the unqualified SOTA claim is not supported by the small and medium variants, and the "lossless" property of the LMIMO framework is not formally established. The empirical evaluation is conducted on external benchmarks, so there is no circularity concern from fitting-then-testing on the same data.

major comments (3)
  1. [Abstract and Section I, Tables II, IV, V, VII] The abstract and Section I claim that the proposed model "outperforms state-of-the-art methods" and achieves SOTA "on most datasets" without specifying which SWFormer variant supports this claim. The tables show that only SWFormer-l supports "most datasets," while SWFormer-s and SWFormer-m, which are the variants highlighted for efficiency in Fig. 1(b) and Table XI, are not SOTA. For example, in Table II SWFormer-s is below NeRD-Rain on DDN-Data (34.18 vs. 34.45 dB) and below SFHformer on SPA-Data (49.03 vs. 50.11 dB); in Table IV SWFormer-s is below SFHformer on all five dehazing benchmarks (ITS 41.86 vs. 43.03 dB, O-HAZE 22.90 vs. 25.81 dB); in Table VII SWFormer-s is below SFHformer on L-400 (30.16 vs. 30.18 dB). Since the efficiency claims are based on SWFormer-s/m, no single configuration is demonstrated to be simultaneously SOTA and the most efficient. The SOTA claim should be qualified per variant, or the paper should provide a Pareto analysis of the s/m/l family. This is a load-bearing issue for the central claim.
  2. [Section III-A, LMIMO] The term "Lossless" in Lossless Multi-Input Multi-Output is not established. The authors state that the multi-input part uses Wavelet Transform or Pixel Unshuffle for downsampling, "which preserves lossless information," but no proof or constraint is given. A wavelet decomposition is lossless only for orthogonal or perfect-reconstruction filter banks. In the wavelet branch, the transform W_phi is described as a "learnable 2D wavelet transform" with parameterized convolutions, and W^{-1} is a separate learnable inverse, with no orthogonality or perfect-reconstruction constraint described. If a fixed orthogonal wavelet (e.g., Haar) is used in the LMIMO input, this should be stated explicitly; if the transform is learnable, the paper should either impose a perfect-reconstruction constraint or provide an empirical reconstruction-error measurement. As written, "lossless" is an unsupported naming claim.
  3. [Section III-B, Eq. (5)] The Fourier-domain branch as written is not mathematically well-defined. The text says that after applying the 2D FFT, the real and imaginary parts are concatenated to produce real tensors F1_j and F2_j in R^{H x W x 2C}, and Eq. (5) then applies the inverse FFT to their element-wise product. The inverse FFT requires a complex input. For Eq. (5) to be well-posed, the authors must state that the 2C channels are split into real and imaginary halves and that the gating mechanism operates on each half, or they should formulate the branch using C-channel complex tensors. Without this clarification, the core Fourier branch cannot be reproduced from the description.
minor comments (4)
  1. [Section IV-L, Table XII] The caption of Table XII reads "Experimental Results on Model Complexity," but the table reports inference latency and PSNR/SSIM; the caption should be renamed to "Inference Latency" or similar.
  2. [Section III-A] The notation {I_i, i = 1, 2, 3} in {R^{H/n x W/n x 3 x 2^{n-1}}, n = 1, 2, 3} is confusing: the expression 3 x 2^{n-1} appears to refer to the number of channels after wavelet decomposition, but writing it as a set membership over n is not standard and should be clarified with an explicit per-scale channel mapping.
  3. [Section IV-M, Eq. (6)] The multi-domain loss in Eq. (6) introduces a hyperparameter lambda set to 0.1, but no ablation or sensitivity study for lambda is reported; the authors should at least state whether the results are stable with respect to this choice.
  4. [Throughout] There are several typos and minor language issues: "As reflected by the its name" in Section III-B, "we replaces self-attention" in the Conclusion, "themself" in Section II-A, and a stray space in the Section IV-F heading "F . Results on Defocus Deblurring Task." These should be corrected in a final revision.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SWFormer's contributions are an empirical architecture plus external-benchmark evaluation, with no fitted quantity renamed as a prediction.

full rationale

SWFormer's contribution is an architecture plus an empirical evaluation; there is no derivation chain in which an output quantity is constructed from its own target. The multi-domain prior in Section I is motivated by the in-paper swap experiment in Fig. 1(c), not by a fitted parameter or by a cited theorem. The loss in Eq. (6) is a training objective (spatial/wavelet/Fourier L1 terms with lambda = 0.1), and the reported PSNR/SSIM values are measured on held-out external benchmarks (RESIDE, GoPro, DPDD, LOL, etc.); no 'prediction' is the fit itself. Ablations in Tabs. XIII-XV compare components under matched complexity rather than asserting components by construction. The authors' own prior work [43] is cited as a baseline and as the conference version of this model; that self-citation is not load-bearing in the argument, since the comparison numbers are empirical and the architectural changes (wavelet branch, gating filter, LMIMO) are separately ablated. No uniqueness theorem or authority chain forces the architecture. Thus no circular step meeting the evidentiary standard is present.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The central claim rests on standard transform theory, the unproven invertibility of the learned wavelet branch, and the generalization of a qualitative prior. The only fitted constant is the loss weight lambda.

free parameters (1)
  • loss weight lambda = 0.1
    Chosen by hand to balance spatial, wavelet, and Fourier L1 losses in Eq. (6).
assumptions (3)
  • standard math Standard wavelet and Fourier transforms preserve information needed for reconstruction.
    Invoked in Section III-A and the wavelet/Fourier branch descriptions; assumes lossless decomposition for downsampling and upsampling.
  • domain assumption The learnable wavelet transform W_phi used in the wavelet branch is effectively invertible (the paper calls it 'lossless').
    Section III-A and Fig. 4(b) describe a learnable 2D wavelet transform with parameterized convolutions, but no constraint or proof is given that the learned transform and its inverse reconstruct exactly.
  • ad hoc to paper The qualitative degradation-separation prior observed in Fig. 1(c) generalizes to all ten restoration tasks.
    The architecture is motivated by the claim that degradation concentrates in specific wavelet sub-bands and Fourier frequencies, but this is shown on a few examples and not statistically validated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Image Restoration via Multi-domain Learning." pith.science (2026). https://pith.science/paper/C5JI4KB3

@misc{pith2026250505504,
  author       = {Pith},
  title        = {Pith review of: Image Restoration via Multi-domain Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C5JI4KB3}},
  note         = {Machine review of arXiv:2505.05504}
}
read the original abstract

Due to adverse atmospheric and imaging conditions, natural images suffer from various degradation phenomena. Consequently, image restoration has emerged as a key solution and garnered substantial attention. Although recent Transformer architectures have demonstrated impressive success across various restoration tasks, their considerable model complexity poses significant challenges for both training and real-time deployment. Furthermore, instead of investigating the commonalities among different degradations, most existing restoration methods focus on modifying Transformer under limited restoration priors. In this work, we first review various degradation phenomena under multi-domain perspective, identifying common priors. Then, we introduce a novel restoration framework, which integrates multi-domain learning into Transformer. Specifically, in Token Mixer, we propose a Spatial-Wavelet-Fourier multi-domain structure that facilitates local-region-global multi-receptive field modeling to replace vanilla self-attention. Additionally, in Feed-Forward Network, we incorporate multi-scale learning to fuse multi-domain features at different resolutions. Comprehensive experimental results across ten restoration tasks, such as dehazing, desnowing, motion deblurring, defocus deblurring, rain streak/raindrop removal, cloud removal, shadow removal, underwater enhancement and low-light enhancement, demonstrate that our proposed model outperforms state-of-the-art methods and achieves a favorable trade-off among restoration performance, parameter size, computational cost and inference latency. The code is available at: https://github.com/deng-ai-lab/SWFormer.

Figures

Figures reproduced from arXiv: 2505.05504 by the authors.

Figure 1
Figure 1. (a) Spatial-domain representations for various restoration tasks. (b) Performance and model complexity balance: Average [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Inter-block designs: (a) Single-Input Single-Output, (b) Multi-Input Multi-Output, (c) Lossless Multi-Input Multi-Output. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of Token Mixer and FFN modules across various restoration methods at Intra-block level. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: The overall framework of our proposed SWFormer and its detailed components. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The quantitative evaluation results on synthetic/real-world rain streak removal and raindrop removal. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The quantitative evaluation results on synthetic and real-world low-light enhancement. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: The quantitative evaluation results on synthetic and real-world image dehazing. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: The quantitative evaluation results on motion deblurring. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: The quantitative evaluation results on defocus deblurring. [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: The quantitative evaluation results on underwater enhancement. [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: The quantitative evaluation results on image desnowing. [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: The quantitative evaluation results on shadow removal. [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Quantitative evaluation results on cloud removal. [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

112 extracted references · 73 canonical work pages

  1. [1]

    In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

    Abuolaim, A., Afifi, M., Brown, M.S.: Improving single-image defocus deblurring: How dual-pixel images help through multi-task learning. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1231–1239 (2022)

    2022

  2. [2]

    Abuolaim, A., Brown, M.S.: Defocus deblurring using dual-pixel data. In: Eur. Conf. Comput. Vis. pp. 111–126. Springer (2020)

    2020

  3. [3]

    In: 2019 IEEE international conference on image processing (ICIP)

    Ancuti, C.O., Ancuti, C., Sbert, M., Timofte, R.: Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images. In: 2019 IEEE international conference on image processing (ICIP). pp. 1014–1018. IEEE (2019)

    2019

  4. [4]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops

    Ancuti, C.O., Ancuti, C., Timofte, R.: Nh-haze: An image dehazing benchmark with non-homogeneous hazy and haze-free images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 444–445 (2020)

    2020

  5. [5]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops

    Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C.: O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 754–762 (2018)

    2018

  6. [6]

    arXiv preprint arXiv:2303.06705 (2023)

    Cai, Y ., Bian, H., Lin, J., Wang, H., Timofte, R., Zhang, Y .: Retinex- former: One-stage retinex-based transformer for low-light image en- hancement. arXiv preprint arXiv:2303.06705 (2023)

    arXiv 2023

  7. [7]

    arXiv preprint arXiv:2405.17074 (2024)

    Chen, H., Chen, X., Wu, C., Zheng, Z., Pan, J., Fu, X.: Towards ultra- high-definition image deraining: A benchmark and an efficient method. arXiv preprint arXiv:2405.17074 (2024)

    arXiv 2024

  8. [8]

    Chen, L., Chu, X., Zhang, X., Sun, J.: Simple baselines for image restoration. In: Eur. Conf. Comput. Vis. pp. 17–33. Springer (2022)

    2022

Show all 112 references
  1. [9]

    Chen, S., Ye, T., Bai, J., Chen, E., Shi, J., Zhu, L.: Sparse sampling transformer with uncertainty-driven ranking for unified removal of raindrops and rain streaks. In: Int. Conf. Comput. Vis. pp. 13106– 13117 (2023)

    2023

  2. [10]

    Chen, W.T., Fang, H.Y ., Ding, J.J., Tsai, C.C., Kuo, S.Y .: Jstasr: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. In: Eur. Conf. Comput. Vis. pp. 754–770. Springer (2020)

    2020

  3. [11]

    Chen, W.T., Fang, H.Y ., Hsieh, C.L., Tsai, C.C., Chen, I., Ding, J.J., Kuo, S.Y ., et al.: All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In: Int. Conf. Comput. Vis. pp. 4196–4205 (2021)

    2021

  4. [12]

    In: IEEE Conf

    Chen, X., Li, H., Li, M., Pan, J.: Learning a sparse transformer network for effective image deraining. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5896–5905 (2023)

    2023

  5. [13]

    In: IEEE Conf

    Chen, X., Pan, J., Dong, J.: Bidirectional multi-scale implicit neural representations for image deraining. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 25627–25636 (2024)

    2024

  6. [14]

    In: Proceedings of the AAAI conference on artificial intelligence

    Chen, X., Pan, J., Lu, J., Fan, Z., Li, H.: Hybrid cnn-transformer feature fusion for single image deraining. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, pp. 378–386 (2023)

    2023

  7. [15]

    Chen, X., Li, Z., Pu, Y ., Liu, Y ., Zhou, J., Qiao, Y ., Dong, C.: A comparative study of image restoration networks for general backbone network design. In: Eur. Conf. Comput. Vis. pp. 74–91. Springer (2024)

    2024

  8. [16]

    Cho, S.J., Ji, S.W., Hong, J.P., Jung, S.W., Ko, S.J.: Rethinking coarse- to-fine approach in single image deblurring. In: Int. Conf. Comput. Vis. pp. 4641–4650 (2021)

    2021

  9. [17]

    Chu, X., Chen, L., Chen, C., Lu, X.: Improving image restoration by revisiting global information aggregation. In: Eur. Conf. Comput. Vis. pp. 53–71. Springer (2022)

    2022

  10. [18]

    Cui, Y ., Ren, W., Cao, X., Knoll, A.: Focal network for image restoration. In: Int. Conf. Comput. Vis. pp. 13001–13011 (2023)

    2023

  11. [19]

    Cui, Y ., Ren, W., Yang, S., Cao, X., Knoll, A.: Irnext: Rethinking convolutional network design for image restoration. In: Int. Conf. Mach. Learn. (2023)

    2023

  12. [20]

    Cui, Y ., Tao, Y ., Bing, Z., Ren, W., Gao, X., Cao, X., Huang, K., Knoll, A.: Selective frequency network for image restoration. In: Int. Conf. Learn. Represent. (2022)

    2022

  13. [21]

    In: Proceedings of the AAAI conference on artificial intelligence

    Cun, X., Pun, C.M., Shi, C.: Towards ghost-free shadow removal via dual hierarchical aggregation network and shadow matting gan. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 10680–10687 (2020)

    2020

  14. [22]

    In: Proceedings of the Asian Conference on Computer Vision

    Ding, H., Zi, Y ., Xie, F.: Uncertainty-based thin cloud removal network via conditional variational autoencoders. In: Proceedings of the Asian Conference on Computer Vision. pp. 469–485 (2022)

    2022

  15. [23]

    In: IEEE Conf

    Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., Yang, M.H.: Multi-scale boosted dehazing network with dense feature fusion. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2157–2167 (2020)

    2020

  16. [24]

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: Int. Conf. Learn. Represent. (2020)

    2020

  17. [25]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops

    Engin, D., Genc ¸, A., Kemal Ekenel, H.: Cycle-dehaze: Enhanced cyclegan for single image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 825–833 (2018)

    2018

  18. [26]

    In: IEEE Conf

    Fu, L., Zhou, C., Guo, Q., Juefei-Xu, F., Yu, H., Feng, W., Liu, Y ., Wang, S.: Auto-exposure fusion for single-image shadow removal. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 10571–10580 (2021) JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 15

    2021

  19. [27]

    In: IEEE Conf

    Fu, X., Huang, J., Zeng, D., Huang, Y ., Ding, X., Paisley, J.: Removing rain from single images via a deep detail network. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 3855–3863 (2017)

    2017

  20. [28]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Fu, X., Qi, Q., Zha, Z.J., Zhu, Y ., Ding, X.: Rain streak removal via dual graph convolutional network. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 1352–1360 (2021)

    2021

  21. [29]

    Fu, Z., Wang, W., Huang, Y ., Ding, X., Ma, K.K.: Uncertainty inspired underwater image enhancement. In: Eur. Conf. Comput. Vis. pp. 465–

  22. [30]

    In: IEEE Conf

    Fu, Z., Yang, Y ., Tu, X., Huang, Y ., Ding, X., Ma, K.K.: Learning a simple low-light image enhancer from paired low-light instances. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 22252–22261 (2023)

    2023

  23. [31]

    Gao, N., Jiang, X., Zhang, X., Deng, Y .: Efficient frequency-domain image deraining with contrastive regularization. In: Eur. Conf. Comput. Vis. pp. 240–257. Springer (2024)

    2024

  24. [32]

    In: IEEE Conf

    Guo, C.L., Yan, Q., Anwar, S., Cong, R., Ren, W., Li, C.: Image dehazing transformer with transmission-aware 3d position embedding. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5812–5820 (2022)

    2022

  25. [33]

    Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., Xia, S.T.: Mambair: A simple baseline for image restoration with state-space model. In: Eur. Conf. Comput. Vis. pp. 222–241. Springer (2024)

    2024

  26. [34]

    In: Proceedings of the AAAI conference on artificial intelligence

    Guo, L., Huang, S., Liu, D., Cheng, H., Wen, B.: Shadowformer: global context helps shadow removal. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, pp. 710–718 (2023)

    2023

  27. [35]

    In: IEEE Conf

    Guo, L., Wang, C., Yang, W., Huang, S., Wang, Y ., Pfister, H., Wen, B.: Shadowdiffusion: When degradation prior meets diffusion model for shadow removal. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 14049–14058 (2023)

    2023

  28. [36]

    IEEE Trans

    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341– 2353 (2010)

    2010

  29. [37]

    In: IEEE Conf

    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 770–778 (2016)

    2016

  30. [38]

    Optics communications 282(11), 2123–2127 (2009)

    Huang, C.K., Nien, H.H.: Multi chaotic systems based pixel shuffle for image encryption. Optics communications 282(11), 2123–2127 (2009)

    2009

  31. [39]

    In: IEEE Conf

    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 4700–4708 (2017)

    2017

  32. [40]

    IEEE Trans

    Huang, S.C., Le, T.H., Jaw, D.W.: Dsnet: Joint semantic learning for object detection in inclement weather conditions. IEEE Trans. Pattern Anal. Mach. Intell. 43(8), 2623–2633 (2020)

    2020

  33. [41]

    Huo, F., Li, B., Zhu, X.: Efficient wavelet boost learning-based multi- stage progressive refinement network for underwater image enhance- ment. In: Int. Conf. Comput. Vis. pp. 1944–1952 (2021)

    2021

  34. [42]

    Jiang, X., Dou, H., Fu, C., Dai, B., Xu, T., Deng, Y .: Boosting supervised dehazing methods via bi-level patch reweighting. In: Eur. Conf. Comput. Vis. pp. 57–73. Springer (2022)

    2022

  35. [43]

    Jiang, X., Zhang, X., Gao, N., Deng, Y .: When fast fourier transform meets transformer for image restoration. In: Eur. Conf. Comput. Vis. pp. 381–402. Springer (2024)

    2024

  36. [44]

    Jin, Y ., Sharma, A., Tan, R.T.: Dc-shadownet: Single-image hard and soft shadow removal using unsupervised domain-classifier guided network. In: Int. Conf. Comput. Vis. pp. 5027–5036 (2021)

    2021

  37. [45]

    In: IEEE Conf

    Kong, L., Dong, J., Ge, J., Li, M., Pan, J.: Efficient frequency domain- based transformers for high-quality image deblurring. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5886–5895 (2023)

    2023

  38. [46]

    Journal of the Optical society of America 61(1), 1–11 (1971)

    Land, E.H., McCann, J.J.: Lightness and retinex theory. Journal of the Optical society of America 61(1), 1–11 (1971)

    1971

  39. [47]

    Le, H., Samaras, D.: Shadow removal via shadow image decomposi- tion. In: Int. Conf. Comput. Vis. pp. 8578–8587 (2019)

    2019

  40. [48]

    In: IEEE Conf

    Lee, J., Son, H., Rim, J., Cho, S., Lee, S.: Iterative filter adaptive network for single image defocus deblurring. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2034–2042 (2021)

    2021

  41. [49]

    In: Proceedings of the IEEE international conference on computer vision

    Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE international conference on computer vision. pp. 4770–4778 (2017)

    2017

  42. [50]

    IEEE Trans

    Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., Wang, Z.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2018)

    2018

  43. [51]

    IEEE Trans

    Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., Tao, D.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376–4389 (2019)

    2019

  44. [52]

    In: IEEE Conf

    Li, R., Tan, R.T., Cheong, L.F.: All in one bad weather removal using architectural search. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 3175–3185 (2020)

    2020

  45. [53]

    Li, X., Guo, Q., Abdelfattah, R., Lin, D., Feng, W., Tsang, I., Wang, S.: Leveraging inpainting for single-image shadow removal. In: Int. Conf. Comput. Vis. pp. 13055–13064 (2023)

    2023

  46. [54]

    In: IEEE Conf

    Li, Y ., Fan, Y ., Xiang, X., Demandolx, D., Ranjan, R., Timofte, R., Van Gool, L.: Efficient and explicit modelling of image hierarchies for image restoration. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 18278–18289 (2023)

    2023

  47. [55]

    In: IEEE Conf

    Lin, T.Y ., Doll ´ar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2117–2125 (2017)

    2017

  48. [56]

    In: IEEE Conf

    Liu, C., Wang, X., Xu, X., Tian, R., Li, S., Qian, X., Yang, M.H.: Motion-adaptive separable collaborative filters for blind motion deblur- ring. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 25595–25605 (2024)

    2024

  49. [57]

    In: IEEE Conf

    Liu, R., Ma, L., Zhang, J., Fan, X., Luo, Z.: Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhance- ment. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 10561–10570 (2021)

    2021

  50. [58]

    Liu, X., Ma, Y ., Shi, Z., Chen, J.: Griddehazenet: Attention-based multi-scale network for image dehazing. In: Int. Conf. Comput. Vis. pp. 7314–7323 (2019)

    2019

  51. [59]

    In: IEEE Conf

    Liu, X., Suganuma, M., Sun, Z., Okatani, T.: Dual residual networks leveraging the potential of paired operations for image restoration. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 7007–7016 (2019)

    2019

  52. [60]

    arXiv preprint arXiv:2503.23717 (2025)

    Liu, Y ., Li, W., Guan, J., Zhou, S., Zhang, Y .: Effective cloud removal for remote sensing images by an improved mean-reverting denoising model with elucidated design space. arXiv preprint arXiv:2503.23717 (2025)

    2025 arXiv

  53. [61]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Liu, Y ., Ke, Z., Xu, K., Liu, F., Wang, Z., Lau, R.W.: Recasting regional lighting for shadow removal. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 3810–3818 (2024)

    2024

  54. [62]

    IEEE Trans

    Liu, Y .F., Jaw, D.W., Huang, S.C., Hwang, J.N.: Desnownet: Context- aware deep network for snow removal. IEEE Trans. Image Process. 27(6), 3064–3073 (2018)

    2018

  55. [63]

    Liu, Z., Lin, Y ., Cao, Y ., Hu, H., Wei, Y ., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Int. Conf. Comput. Vis. pp. 10012–10022 (2021)

    2021

  56. [64]

    arXiv preprint arXiv:1608.03983 (2016)

    Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)

    2016 arXiv

  57. [65]

    arXiv preprint arXiv:1711.05101 (2017)

    Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

    2017 arXiv

  58. [66]

    In: IEEE Conf

    Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 3883–3891 (2017)

    2017

  59. [67]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Naik, A., Swarnakar, A., Mittal, K.: Shallow-uwnet: Compressed model for underwater image enhancement (student abstract). In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 15853– 15854 (2021)

    2021

  60. [68]

    IEEE Trans

    ¨Ozdenizci, O., Legenstein, R.: Restoring vision in adverse weather conditions with patch-based denoising diffusion models. IEEE Trans. Pattern Anal. Mach. Intell. (2023)

    2023

  61. [69]

    Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inform. Process. Syst. 32 (2019)

    2019

  62. [70]

    IEEE Trans

    Peng, L., Zhu, C., Bian, L.: U-shape transformer for underwater image enhancement. IEEE Trans. Image Process. (2023)

    2023

  63. [71]

    In: IEEE Conf

    Qian, R., Tan, R.T., Yang, W., Su, J., Liu, J.: Attentive generative adversarial network for raindrop removal from a single image. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2482–2491 (2018)

    2018

  64. [72]

    In: Proceedings of the AAAI conference on artificial intelligence

    Qin, X., Wang, Z., Bai, Y ., Xie, X., Jia, H.: Ffa-net: Feature fusion attention network for single image dehazing. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 11908–11915 (2020)

    2020

  65. [73]

    Qiu, Y ., Zhang, K., Wang, C., Luo, W., Li, H., Jin, Z.: Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for image dehazing. In: Int. Conf. Comput. Vis. pp. 12802–12813 (2023)

    2023

  66. [74]

    Quan, Y ., Wu, Z., Ji, H.: Gaussian kernel mixture network for single image defocus deblurring. Adv. Neural Inform. Process. Syst. 34, 20812–20824 (2021)

    2021

  67. [75]

    In: IEEE Conf

    Quan, Y ., Wu, Z., Ji, H.: Neumann network with recursive kernels for single image defocus deblurring. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5754–5763 (2023)

    2023

  68. [76]

    IEEE Trans

    Quan, Y ., Wu, Z., Xu, R., Ji, H.: Deep single image defocus deblurring via gaussian kernel mixture learning. IEEE Trans. Pattern Anal. Mach. Intell. (2024) JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 16

    2024

  69. [77]

    Ren, M., Delbracio, M., Talebi, H., Gerig, G., Milanfar, P.: Multiscale structure guided diffusion for image deblurring. In: Int. Conf. Comput. Vis. pp. 10721–10733 (2023)

    2023

  70. [78]

    IEEE Transactions on Geoscience and Remote Sensing 60, 1–16 (2022)

    Ren, T., Xu, H., Jiang, G., Yu, M., Zhang, X., Wang, B., Luo, T.: Re- inforced swin-convs transformer for simultaneous underwater sensing scene image enhancement and super-resolution. IEEE Transactions on Geoscience and Remote Sensing 60, 1–16 (2022)

    2022

  71. [79]

    IEEE Transactions on Computational Imaging 7, 675–688 (2021)

    Ruan, L., Chen, B., Li, J., Lam, M.L.: Aifnet: All-in-focus image restoration network using a light field-based dataset. IEEE Transactions on Computational Imaging 7, 675–688 (2021)

    2021

  72. [80]

    In: IEEE Conf

    Ruan, L., Chen, B., Li, J., Lam, M.: Learning to deblur using light field generated and real defocus images. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 16304–16313 (2022)

    2022

  73. [81]

    Shen, Z., Wang, W., Lu, X., Shen, J., Ling, H., Xu, T., Shao, L.: Human-aware motion deblurring. In: Int. Conf. Comput. Vis. pp. 5572– 5581 (2019)

    2019

  74. [82]

    Son, H., Lee, J., Cho, S., Lee, S.: Single image defocus deblurring using kernel-sharing parallel atrous convolutions. In: Int. Conf. Comput. Vis. pp. 2642–2650 (2021)

    2021

  75. [83]

    IEEE Trans

    Song, Y ., He, Z., Qian, H., Du, X.: Vision transformers for single image dehazing. IEEE Trans. Image Process. 32, 1927–1941 (2023)

    2023

  76. [84]

    IEEE Transactions on Geoscience and Remote Sensing (2024)

    Sui, J., Ma, Y ., Yang, W., Zhang, X., Pun, M.O., Liu, J.: Diffusion enhancement for cloud removal in ultra-resolution remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing (2024)

    2024

  77. [85]

    In: IEEE Conf

    Szegedy, C., Vanhoucke, V ., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2818–2826 (2016)

    2016

  78. [86]

    Tsai, F.J., Peng, Y .T., Lin, Y .Y ., Tsai, C.C., Lin, C.W.: Stripformer: Strip transformer for fast image deblurring. In: Eur. Conf. Comput. Vis. pp. 146–162. Springer (2022)

    2022

  79. [87]

    In: IEEE Conf

    Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., Li, Y .: Maxim: Multi-axis mlp for image processing. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5769–5780 (2022)

    2022

  80. [88]

    In: IEEE Conf

    Valanarasu, J.M.J., Yasarla, R., Patel, V .M.: Transweather: Transformer-based restoration of images degraded by adverse weather conditions. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2353–2363 (2022)

    2022

  81. [89]

    In: Proceedings of the AAAI conference on artificial intelligence

    Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B., Lu, T.: Ultra-high- definition low-light image enhancement: A benchmark and transformer- based method. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, pp. 2654–2662 (2023)

    2023

  82. [90]

    In: IEEE Conf

    Wang, T., Yang, X., Xu, K., Chen, S., Zhang, Q., Lau, R.W.: Spatial attentive single-image deraining with a high quality real rain dataset. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 12270–12279 (2019)

    2019

  83. [91]

    In: Proceedings of the AAAI conference on artificial intelligence

    Wang, Y ., Wan, R., Yang, W., Li, H., Chau, L.P., Kot, A.: Low- light image enhancement with normalizing flow. In: Proceedings of the AAAI conference on artificial intelligence. vol. 36, pp. 2604–2612 (2022)

    2022

  84. [92]

    In: IEEE Conf

    Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: A general u-shaped transformer for image restoration. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 17683–17693 (2022)

    2022

  85. [93]

    arXiv preprint arXiv:1808.04560 (2018)

    Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560 (2018)

    2018 arXiv

  86. [94]

    IEEE Trans

    Xiao, J., Fu, X., Liu, A., Wu, F., Zha, Z.J.: Image de-raining trans- former. IEEE Trans. Pattern Anal. Mach. Intell. (2022)

    2022

  87. [95]

    In: IEEE Conf

    Xu, X., Wang, R., Fu, C.W., Jia, J.: Snr-aware low-light image enhancement. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 17714– 17724 (2022)

    2022

  88. [96]

    arXiv preprint arXiv:2502.20272 (2025)

    Yan, Q., Feng, Y ., Zhang, C., Pang, G., Shi, K., Wu, P., Dong, W., Sun, J., Zhang, Y .: Hvi: A new color space for low-light image enhancement. arXiv preprint arXiv:2502.20272 (2025)

    2025 arXiv

  89. [97]

    In: IEEE Conf

    Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., Yan, S.: Deep joint rain detection and removal from a single image. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 1357–1366 (2017)

    2017

  90. [98]

    IEEE Trans

    Yang, W., Wang, W., Huang, H., Wang, S., Liu, J.: Sparse gradient reg- ularized deep retinex network for robust low-light image enhancement. IEEE Trans. Image Process. 30, 2072–2086 (2021)

    2021

  91. [99]

    Ye, T., Zhang, Y ., Jiang, M., Chen, L., Liu, Y ., Chen, S., Chen, E.: Perceiving and modeling density for image dehazing. In: Eur. Conf. Comput. Vis. pp. 130–145. Springer (2022)

    2022

  92. [100]

    Yi, Q., Li, J., Dai, Q., Fang, F., Zhang, G., Zeng, T.: Structure- preserving deraining with residue channel prior guidance. In: Int. Conf. Comput. Vis. pp. 4238–4247 (2021)

    2021

  93. [101]

    In: IEEE Conf

    Yu, W., Luo, M., Zhou, P., Si, C., Zhou, Y ., Wang, X., Feng, J., Yan, S.: Metaformer is actually what you need for vision. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 10819–10829 (2022)

    2022

  94. [102]

    IEEE Geoscience and Remote Sensing Letters 19, 1–5 (2022)

    Yu, W., Zhang, X., Pun, M.O.: Cloud removal in optical remote sensing imagery using multiscale distortion-aware networks. IEEE Geoscience and Remote Sensing Letters 19, 1–5 (2022)

    2022

  95. [103]

    In: IEEE Conf

    Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: Efficient transformer for high-resolution image restoration. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5728–5739 (2022)

    2022

  96. [104]

    In: IEEE Conf

    Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Multi-stage progressive image restoration. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 14821–14831 (2021)

    2021

  97. [105]

    IEEE Trans

    Zeng, H., Cai, J., Li, L., Cao, Z., Zhang, L.: Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Trans. Pattern Anal. Mach. Intell. 44(4), 2058–2073 (2020)

    2020

  98. [106]

    In: IEEE Conf

    Zhang, H., Patel, V .M.: Density-aware single image de-raining using a multi-stream dense network. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 695–704 (2018)

    2018

  99. [107]

    IEEE Trans

    Zhang, H., Xiao, L., Cao, X., Foroosh, H.: Multiple adverse weather conditions adaptation for object detection via causal intervention. IEEE Trans. Pattern Anal. Mach. Intell. 46(3), 1742–1756 (2022)

    2022

  100. [108]

    arXiv preprint arXiv:2309.01377 (2023)

    Zhang, X.F., Gu, C.C., Zhu, S.Y .: Memory augment is all you need for image restoration. arXiv preprint arXiv:2309.01377 (2023)

    2023 arXiv

  101. [109]

    In: IEEE Conf

    Zheng, Y ., Zhan, J., He, S., Dong, J., Du, Y .: Curricular contrastive regularization for physics-aware single image dehazing. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5785–5794 (2023)

    2023

  102. [110]

    Zhou, S., Li, C., Change Loy, C.: Lednet: Joint low-light enhancement and deblurring in the dark. In: Eur. Conf. Comput. Vis. pp. 573–589. Springer (2022)

    2022

  103. [111]

    In: IEEE Conf

    Zhou, S., Chen, D., Pan, J., Shi, J., Yang, J.: Adapt or perish: Adaptive sparse transformer with attentive feature refinement for image restoration. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 2952–2963 (2024)

    2024

  104. [112]

    In: IEEE Conf

    Zhu, Y ., Huang, J., Fu, X., Zhao, F., Sun, Q., Zha, Z.J.: Bijective mapping network for shadow removal. In: IEEE Conf. Comput. Vis. Pattern Recog. pp. 5627–5636 (2022) Xingyu Jiang received the B.S. degree in aircraft control and information engineering from the Image Process...

    2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.