{"id":"139906d2-e7e9-497a-85ed-8972605be391","arxiv_id":"2506.18437","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"Dabformer combines wavelet and Gabor filtered attention with an FFT gating network, but the reported gains over prior methods are inconsistent across datasets.","lead":"This paper introduces Dabformer, a transformer-based image restoration network that blends wavelet and Gabor features in attention and replaces the feed-forward network with a learnable FFT filter. Tests on rain and damaged-image datasets show mixed gains, but the paper claims consistent superiority that its own tables do not support.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Table II contradicts the inpainting claim: Dabformer trails StrDiffusion in most heavy-occlusion PSNR/SSIM cells and leads by 0.03 dB with a 0.10 SSIM deficit in its best cell, so the central claim is unsupported as printed.","rationale":"I agree with the reader's concern that the random-noise-block protocol is a nonstandard proxy for inpainting, but the more decisive problem is internal: Table II does not show the consistent improvements the abstract and Section IV-F claim. The highest-occlusion rows on both datasets are won by StrDiffusion on PSNR and SSIM, and the one reported PSNR win (Places2 40–50%) is paired with a substantially lower SSIM. Since the strongest evidence for the central claim is the experimental tables, a direct contradiction there is the load-bearing issue. The missing Gabformer baseline, lack of significance testing, and absence of a direct high-frequency metric reinforce the rejection but are secondary. My proposed test would settle whether the numbers are simply misreported or whether the claim is genuinely unsupported; either way the current manuscript should not be accepted as evidence for improved inpainting. No change to the reader's REJECT verdict is needed.","tokens_in":15514,"tokens_out":5434,"duration_ms":58837,"concrete_test":"Independently reproduce Table II by running the authors' model and the official checkpoints of StrDiffusion, RePaint, and Restormer on the identical 6,000-image Places2 and 4,000-image CelebA random-block test sets, computing paired PSNR/SSIM with confidence intervals. If the printed values reproduce — in particular Dabformer 22.42/0.780 on Places2 40–50% and StrDiffusion 21.75/0.874 on CelebA 60–70% — then the claimed superiority over state-of-the-art inpainting methods is already falsified by the paper's own protocol; if, instead, the paper's numbers do not reproduce, the experimental record must be corrected before any claim is accepted.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract claims that Dabformer effectively improves image inpainting by preserving high-frequency details; the load-bearing evidence is Table II. The table's own numbers fail to support this. On Places2 60–70% occlusion, StrDiffusion scores 20.43 dB/0.858 SSIM versus Dabformer's 20.04 dB/0.792 SSIM; on CelebA 60–70%, StrDiffusion is 21.75/0.874 versus 21.44/0.842; on Places2 40–50%, Dabformer's reported PSNR lead over StrDiffusion is 0.03 dB (22.42 vs 22.39) while its SSIM is 0.780 versus 0.882. The paper's text claims Dabformer achieves the highest PSNR and SSIM under moderate and heavy occlusion, which is contradicted by these rows. Separately, the evaluation protocol — random noise blocks of varying size and position with no mask input — is not the standard free-form-mask inpainting benchmark, so even a clean win would only validate a block-corruption proxy. The claimed high-frequency benefit is also never directly measured. The central contribution is therefore not established by the reported experiments.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes Dabformer, a Transformer-based image inpainting network that integrates wavelet transform and Gabor filtering into a frequency-domain fusion attention mechanism (FDFA) and replaces the feedforward network with a learnable frequency-domain adaptive gating network (FDAGN). The model is evaluated on four deraining datasets and on a 'damaged image restoration' task built by adding random noise blocks of varying size and position to Places2 and CelebA. The authors claim consistent state-of-the-art performance and improved high-frequency preservation for image inpainting.","tokens_in":1269,"tokens_out":2206,"duration_ms":61958,"significance":"The architectural idea of adaptively combining wavelet multiscale decomposition with directional Gabor filtering is timely, and the ablation study suggests that each proposed component contributes to the reported scores. However, the paper's central empirical claim is not established: Table II contradicts the claim of consistent superiority in inpainting, the evaluation protocol diverges from standard free-form-mask inpainting benchmarks, and the claimed high-frequency benefit is never directly measured. The deraining results are also not consistently superior across datasets. The paper would need a substantially revised experimental study to support its claims.","major_comments":[{"comment":"The abstract and Section IV-F2 claim that the proposed method achieves 'consistently superior performance' and the highest PSNR/SSIM under moderate and heavy occlusion. Table II contradicts this: under 60-70% occlusion on Places2, StrDiffusion achieves PSNR/SSIM 20.43/0.858 versus Dabformer's 20.04/0.792; on CelebA 60-70%, StrDiffusion achieves 21.75/0.874 versus 21.44/0.842; on Places2 40-50%, Dabformer's PSNR lead over StrDiffusion is 0.03 dB (22.42 vs 22.39) while its SSIM is lower (0.780 vs 0.882). The central inpainting claim is therefore unsupported by the paper's own numbers.","section":"Section IV-F2, Table II"},{"comment":"The damaged image restoration protocol corrupts each image with 'noise blocks of varying size and position' and does not provide a mask to the model. This is a blind restoration task, not the standard free-form-mask inpainting benchmark used in the field. Consequently, even if the results were strong, they would not establish the abstract's claim about image inpainting. The paper should evaluate on standard inpainting masks (for example, irregular masks) and compare with inpainting-specific methods.","section":"Section IV-B"},{"comment":"The text claims that on sparse-rain datasets 'our method still maintains a leading overall performance,' but Table I shows FADformer outperforms Dabformer on Rain200L (41.69/0.990 vs 41.66/0.990) and DDN-Data (34.42/0.960 vs 34.09/0.957). The claim of consistent superiority in deraining is inaccurate.","section":"Section IV-E, Table I"},{"comment":"The paper claims that the method preserves high-frequency information, but no frequency-domain metric or analysis is provided. The only evidence is PSNR and SSIM, which do not directly measure high-frequency fidelity. Please include spectral comparisons or a frequency-domain error metric to support the mechanism that is central to the paper's title and abstract.","section":"Section III and Section IV"}],"minor_comments":[{"comment":"Only Q is defined in Eq. (3); K and V are used in Eq. (4) but never defined. Please state explicitly how K and V are computed from the input feature maps.","section":"Section III-B, Eq. (3)"},{"comment":"The definition L_M = 1 - SSIM(O) is incomplete because SSIM takes two images; it should be written as 1 - SSIM(G_t, O).","section":"Section III-D, Eq. (8)"},{"comment":"The text below Eq. (1) refers to 'GTFFN modules', but the proposed module is called FDAGN. Please align the terminology.","section":"Section III-A, Eq. (1)"},{"comment":"The parameter count reported for Dabformer (29.73M) is identical to that of ICT (29.73M). Please verify that this is not a typo and provide the correct value.","section":"Table II"},{"comment":"The noise-block corruption procedure is underspecified: the sizes, positions, number of blocks, and whether the blocks are contiguous are not stated. Please provide full details for reproducibility.","section":"Section IV-B"}],"recommendation":"reject","confidential_remarks":"The manuscript leans heavily on the authors' prior Gabformer work [26] for the FDAGN operations, yet the empirical evidence for the claimed improvement is not convincing. The central claim is contradicted by the authors' own Table II, and the evaluation protocol does not follow standard inpainting benchmarks. I would not encourage resubmission without fundamentally new experiments and a revised evaluation protocol. The exact parameter-count match with ICT in Table II also warrants verification."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a plausible incremental architecture paper whose own tables don't support its headline claim. The wavelet+Gabor+FFT-gating combination is new as a configuration, and the ablations are decent. But the inpainting evaluation is nonstandard and the numbers contradict 'consistently superior'.\n\nWhat's actually new: the exact design where wavelet-decomposed queries are fed through direction-matched Gabor filters, with an FFT-based gating network replacing the feedforward network. That's a real, if modest, architectural idea. The ablation study (Tables III and IV) does show each component adds something: wavelet queries help, Gabor helps, the FDAGN helps, and the loss stack helps. The Gabor wavelength sensitivity study is also a nice touch. The deraining results are competitive: on Rain200H and DID-Data it edges out FADformer, and it's within 0.03-0.33 dB elsewhere. So the method is not obviously broken.\n\nWhere it falls down: Table II is the inpainting evidence, and it doesn't support the abstract. On Places2, StrDiffusion beats Dabformer on SSIM in every occlusion range and on PSNR in two of three, sometimes by large margins (0.858 vs 0.792 at 60-70%). On CelebA, Dabformer wins the 40-50% cell decisively but loses the other two. Claiming 'consistently superior performance' is simply false. Also, the 'damaged image restoration' protocol uses random noise blocks with no mask input, which is not standard inpainting. And the closest prior work, the authors' own Gabformer, is absent from every comparison. The claimed high-frequency benefit is never directly measured, just inferred from PSNR/SSIM. These are load-bearing problems for an empirical claim.\n\nIf you're going to referee it: the fix is straightforward in principle — include Gabformer, run standard free-form mask benchmarks (e.g., Places2/CelebA with irregular masks), report mean±std over multiple runs, and cut the claims to what the numbers show. The architecture is worth a look; the evidence as printed is not.\n\nRecommendation: send it to peer review if you expect heavy revision, because the core idea is a legitimate incremental contribution and the ablations are honest. But it should not be accepted in its current form.","headline":"Incremental but real architecture; the paper's own Table II contradicts its 'consistently superior' claim, so it needs major revision plus the missing Gabformer baseline.","tokens_in":16313,"tokens_out":2618,"would_cite":false,"duration_ms":27937,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A Transformer inpainting architecture that fuses wavelet and Gabor frequency features preserves high-frequency texture better than self-attention alone.","keywords":["image inpainting","frequency-domain fusion","wavelet transform","Gabor filter","fast Fourier transform","Transformer","image deraining","high-frequency details"],"falsifier":"Run Dabformer on Places2 and CelebA with free-form irregular masks, holding everything else fixed, and compare PSNR and SSIM against Restormer, StrDiffusion, and FADformer. If the gains seen under random noise blocks shrink, vanish, or reverse on irregular masks, then the reported superiority is tied to the square-block corruption model rather than to general inpainting.","tokens_in":15304,"feed_emoji":"🖼️","tokens_out":6084,"duration_ms":63253,"temperature":0.7,"pith_summary":"Image inpainting with Transformers tends to smooth over fine texture because self-attention behaves like a low-pass filter, and it is computationally heavy. This paper tries to fix both problems in one architecture, Dabformer, by giving the attention mechanism a frequency-domain fusion front end and by replacing the feedforward network with a learnable FFT-based gating filter. The proposed modules decompose features with a wavelet transform, apply directionally matched Gabor filters to the high-frequency subbands, and then carry out cross-channel attention, so the model can see both global structure and directional detail. On deraining benchmarks and on images corrupted with random noise blocks, the reported PSNR and SSIM numbers match or beat strong baselines including a diffusion model, with the largest gains on dense, directional rain and heavy occlusion. The paper's central claim is that frequency-domain fusion is what preserves high-frequency information that ordinary Transformer inpainting loses.","feed_headline":"Frequency-domain fusion keeps Transformer inpainting sharp","feed_subtitle":"Wavelet decomposition plus Gabor direction filters and an FFT gating net preserve fine detail in rain and damaged images.","key_machinery":"The load-bearing mechanism is the Frequency-Domain Fusion Attention (FDFA) paired with the Frequency-Domain Adaptive Gating Network (FDAGN). FDFA applies a discrete wavelet transform to the query, keeps the low-frequency LL subband for a depthwise convolution, and runs Gabor filters on the HL, LH, and HH subbands with a learnable wavelength so that filter scale adapts to local texture; the resulting query drives cross-channel attention of complexity $O(C\\times C)$ rather than $O(M\\times M)$. FDAGN replaces the standard feedforward network: it transforms feature blocks with the fast Fourier transform, applies a learnable complex filter initialized near identity, transforms back, and gates the result with GELU, which lets the network suppress redundant frequencies while preserving structure. The combination is what carries the paper's claim that high-frequency detail survives inpainting.","core_discovery":"On its own terms, the paper claims that the low-pass behavior of standard Transformer attention is the main obstacle to high-quality image inpainting, and that this obstacle can be removed by fusing two classical frequency tools into the Transformer. A wavelet decomposition splits the query into one low-frequency and three high-frequency subbands; Gabor filters aligned with each high-frequency subband's orientation extract directional textures; and a learnable FFT-domain filter takes over the feedforward network's role, adaptively suppressing noisy frequency components while retaining useful ones. Guided by a combination of L1, perceptual, edge, and SSIM losses, the four-level encoder-decoder then produces restorations that, in the paper's experiments, preserve more high-frequency detail than the compared methods. The authors present Dabformer as an extension of their earlier Gabor-guided deraining model, generalized from rain removal to damaged-image restoration.","pith_inferences":["Editorial inference: The same FDFA and FDAGN modules are likely transferable to other pixel-level restoration tasks such as super-resolution and deblurring, because the problem they solve—retaining high-frequency texture while suppressing noise—is not specific to rain or square-block damage.","Editorial inference: Because the damage model uses random square blocks rather than free-form masks, the method's inpainting claim would be tested more sharply on irregular-mask benchmarks; the texture-preservation advantage may or may not survive that change.","Editorial inference: The learnable wavelength in the Gabor filters suggests a natural extension to learnable orientation as well, which could handle textures whose directions do not align with the three fixed subband orientations."],"forward_implications":["On dense, directional rain (Rain200H, DID-Data), the method reports the best or near-best PSNR and SSIM among the compared deraining models.","Under 40–70% occlusion on Places2 and CelebA, Dabformer matches or exceeds diffusion-based inpainting baselines on PSNR and SSIM while using far fewer parameters (29.73M versus 114.05M for StrDiffusion).","Ablations show that combining wavelet and Gabor query features raises PSNR from 23.80 to 24.71 dB on CelebA at 40–50% occlusion, and adding the FFT gating network raises it further to 26.62 dB.","Adding perceptual, edge, and SSIM losses on top of L1 raises Rain200H PSNR from 31.97 to 32.34 dB, so each loss term contributes to detail preservation."],"supporting_citations":[{"why":"Supplies the Restormer baseline and the cross-channel attention design that the FDFA attention simplifies.","marker":"[12]"},{"why":"FADformer is the closest frequency-domain deraining baseline that Dabformer must beat or match.","marker":"[36]"},{"why":"Gabformer is the authors' prior Gabor-guided deraining model whose gating structure the FDAGN extends.","marker":"[26]"},{"why":"Provides the wavelet transform as the multi-scale decomposition tool for separating low- and high-frequency subbands.","marker":"[13]"},{"why":"Provides Gabor filters as the directional texture extractor applied to the high-frequency subbands.","marker":"[14]"},{"why":"Supplies the Rain200H and Rain200L deraining data used for training and testing.","marker":"[37]"},{"why":"Supplies the Places2 scene images used in the damaged-image restoration experiments.","marker":"[40]"},{"why":"Supplies the CelebA face images used in the damaged-image restoration and ablation experiments.","marker":"[41]"}],"fun_headline_variants":["Wavelet-Gabor-FFT fusion sharpens Transformer inpainting","Frequency fusion rescues Transformer inpainting detail","Transformer inpainting gains sharpness via frequency fusion","Fusing wavelets, Gabor, FFT keeps inpainting crisp","Frequency-domain fusion boosts Transformer detail retention"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The inpainting experiments corrupt each image with random noise blocks of varying size and position, and the central claim assumes this synthetic corruption is a fair stand-in for real missing regions and for the irregular mask patterns used in standard inpainting benchmarks.","fun_headline_variants_meta":{"raw":{"variants":["Wavelet-Gabor-FFT fusion sharpens Transformer inpainting","Frequency fusion rescues Transformer inpainting detail","Transformer inpainting gains sharpness via frequency fusion","Fusing wavelets, Gabor, FFT keeps inpainting crisp","Frequency-domain fusion boosts Transformer detail retention"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000272,"raw_usage":{"total_tokens":1601,"prompt_tokens":881,"completion_tokens":720,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":497,"completion_tokens_details":{"reasoning_tokens":639}},"tokens_in":497,"tokens_out":720,"duration_ms":8215,"temperature":1.0,"reasoning_tokens":639,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T23:15:09.270819+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run Dabformer on Places2 and CelebA with free-form irregular masks, holding everything else fixed, and compare PSNR and SSIM against Restormer, StrDiffusion, and FADformer. If the gains seen under random noise blocks shrink, vanish, or reverse on irregular masks, then the reported superiority is tied to the square-block corruption model rather than to general inpainting.","supporting_citations":[{"cited_title":"Efficient frequency- domain image deraining with contrastive regularization,","cited_arxiv_id":null,"evidence_quote":"FADformer is the closest frequency-domain deraining baseline that Dabformer must beat or match."},{"cited_title":"Gabor-guided transformer for single image deraining,","cited_arxiv_id":null,"evidence_quote":"Gabformer is the authors' prior Gabor-guided deraining model whose gating structure the FDAGN extends."},{"cited_title":"A practical guide to wavelet analysis,","cited_arxiv_id":null,"evidence_quote":"Provides the wavelet transform as the multi-scale decomposition tool for separating low- and high-frequency subbands."},{"cited_title":"A review of convolutional neural networks and gabor filters in object recognition,","cited_arxiv_id":null,"evidence_quote":"Provides Gabor filters as the directional texture extractor applied to the high-frequency subbands."}],"review_version":1}