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arxiv: 2604.23508 · v1 · submitted 2026-04-26 · 💻 cs.CV

Recognition: unknown

BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors

Dong Huo , Tristan Aumentado-Armstrong , Samrudhdhi B. Rangrej , Maitreya Suin , Angela Ning Ye , Zhiming Hu , Amanpreet Walia , Amirhossein Kazerouni
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Konstantinos G. Derpanis Iqbal Mohomed Alex Levinshtein
Authors on Pith no claims yet

Pith reviewed 2026-05-08 06:48 UTC · model grok-4.3

classification 💻 cs.CV
keywords burst image super-resolutiondiffusion modelsgenerative priorsvideo priorsraw image processingdegradation-aware conditioningperceptual image quality
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The pith

BurstGP shows that generative priors from pretrained video diffusion models can be transferred to raw burst super-resolution through multiframe conditioning and color-space inversion to recover richer textures with minimal fidelity loss.

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

The paper develops BurstGP as a way to take any standard burst image super-resolution pipeline and layer on top of it a diffusion model that borrows realistic detail generation from models already trained on video. The approach adds a degradation-aware conditioning step that tells the model how much detail to synthesize based on the input burst quality, plus an inverter that lets video-trained priors operate on raw camera data and produce linear RGB output. A sympathetic reader would care because burst super-resolution is the core technique behind high-resolution output from short-exposure smartphone and camera bursts, where current methods often produce oversmoothed results that lose fine structure.

Core claim

The central claim is that a multiframe-aware diffusion model placed atop a conventional BISR method, equipped with degradation-aware conditioning and an sRGB-to-lRGB inverter, successfully adapts pretrained video generative priors to raw burst inputs. This produces high-resolution images that outperform prior art on perceptual metrics such as MUSIQ and LPIPS while recovering richer textures and finer structural details, all with only minimal deviation from the measurements in the original burst frames.

What carries the argument

The multiframe-aware diffusion model with degradation-aware conditioning and the sRGB-to-lRGB inverter, which together let video priors enhance a base burst super-resolution result.

If this is right

  • BurstGP produces higher scores on perceptual metrics including MUSIQ and LPIPS than existing state-of-the-art burst super-resolution methods.
  • The outputs contain richer textures and finer structural details than those from task-specific diffusion models or single-frame approaches.
  • Video priors prove effective for burst image super-resolution even when the model is not trained from scratch on burst data.
  • The added generative detail does not substantially compromise fidelity to the measurements present in the raw input frames.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar conditioning and inverter steps could let the same video priors support other multi-frame raw-image tasks such as joint denoising and deblurring.
  • If the inverter remains stable across different sensor responses, larger video foundation models could be plugged into existing burst pipelines without full retraining.
  • The method invites testing on bursts captured under more varied lighting or motion conditions to check whether the perceptual gains hold when degradation estimation becomes harder.

Load-bearing premise

Pretrained diffusion priors from video can be transferred to raw burst inputs through the proposed multiframe-aware conditioning and sRGB-to-lRGB inverter while adding realistic detail with only minimal loss of fidelity to the original measurements.

What would settle it

A direct test would be to remove the degradation-aware conditioning or the inverter from BurstGP and measure whether perceptual metric gains and visual texture improvements disappear or whether output fidelity to the input burst measurements drops sharply on standard burst super-resolution benchmarks.

Figures

Figures reproduced from arXiv: 2604.23508 by Alex Levinshtein, Amanpreet Walia, Amirhossein Kazerouni, Angela Ning Ye, Dong Huo, Iqbal Mohomed, Konstantinos G. Derpanis, Maitreya Suin, Samrudhdhi B. Rangrej, Tristan Aumentado-Armstrong, Zhiming Hu.

Figure 1
Figure 1. Figure 1: Controlling the perception–distortion trade-off with BurstGP. view at source ↗
Figure 2
Figure 2. Figure 2: We begin with super-resolving a raw LR burst {Ii} N−1 i=0 into a linear RGB (lRGB) SR burst using a Burst Image Super-Resolution (BISR) model. We super￾resolve each frame separately by assuming it to be the reference frame and treating others as source frames. Let the LR and SR versions of the actual reference frame be Llr and Lb. We render the lRGB SR burst in sRGB color-space using an ISP module, denoted… view at source ↗
Figure 3
Figure 3. Figure 3: Visualizations on SyntheticBurst. Patches are shown at increased brightness for better visualization. We report full-image MUSIQ (bottom-left corner). BurstGP consistently improves textural detail, but avoids hallucinating unrealistic content. of λr is more intuitive, showing a linear interpolation between the BISR and diffusion outputs. Additional visualizations are provided in the supplement. Evaluating … view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results on the BurstSR dataset. We report MUSIQ for the entire image. We see that BurstGP is able to reduce blur in the BISR outputs and improve colour fidelity (upper insets), improve noisy or distorted content (lower insets, black structure), and sometimes infer missing details (lower insets, grey grid). instead reflects the compressive and partially non-injective nature of the ISP. As a cons… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on the SyntheticBurst dataset with/without permutation training. Without permutation training, the BISR model is unable to recover details in non-reference frames, even those it is able to recover for the reference frame (first two examples). The model also produces artifacts in non-reference frames without permutation training (last example). Permutation training solves these issues. D… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results on the SyntheticBurst dataset with/without finetuning dif￾fusion model. We use BSRT-L as the BISR model. We report MUSIQ for the entire image. We observe severe artifacts when diffusion model is not finetuned, which we correct in BurstGP by finetuning the diffusion model on burst dataset. Ideally, the diffusion model should behave conservatively when the BISR reconstruction is already p… view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative results on the BurstSR dataset with/without finetuning the dif￾fusion model. We use BSRT-L as the BISR model. We report MUSIQ for the entire image. We observe severe artifacts when the diffusion model is not finetuned, which we correct in BurstGP by finetuning the diffusion model on a burst dataset. to the learned degradation-conditioned embedding. The results indicate that the learned embeddin… view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative results from BurstGP with BurstM, from high fidelity (low λt) (left) to high perceptual quality (high λt) (right). Similar to λr, we observe that low λt stays closer to the base BISR model output, which has less detail and is slightly oversmoothed, while high λt introduces additional sharp texture to the image, as the network assumes additional image content needs to be generated. (zoom-in for … view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative results of two-stage correction scheme in the robust inverse ISP. We re-render the linear outputs back to sRGB space with the ISP operator for visualization. The initial first-order updates in the 1st stage exhibit instability for ill-conditioned pix￾els (particularly in saturated regions), leading to overflow-inducing residual estimates. The TSVD refinement in the 2nd stage effectively suppres… view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative results on the SyntheticBurst dataset. We report MUSIQ for the entire image. We brighten insets for all examples for better visibility. Across models, the addition of our BurstGP method is able to improve denoising and correct distortions (e.g., the first image set), generate plausible textures (e.g., the wall of the second set), and augment blurred content with new details (e.g., the tree in … view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative results on BurstSR. We report MUSIQ for the entire image. We brighten the bottom two examples for better visibility. In all cases, BurstGP reduces blur and oversmoothing (e.g., the lines of the first image set). Further, it can mitigate artifacts from the base model, resolving real details closer to the GT (e.g., the lines on the clothes in the second set, as well as the blurred horizontal lin… view at source ↗
Figure 12
Figure 12. Figure 12: Visualization of the qualitative effects of the single-frame diffusion model, compared to our full multiframe model, on SyntheticBurst (top row) and BurstSR (bottom two rows). We observe that access to the full burst (post-BISR processing) allows the multiframe model to repair certain defects in the single-frame version, such as oversmoothing (first row) or incorrect image content (last two rows) view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative Examples from the RealBSR-RAW test set of real bursts. From left to right: bicubic, BurstM, FBANet, BurstGP-FBANet (ours), and the GT. We observe that our method is able to sharpen and deblur the FBANet output, without excessive hallucinations (rows one and two). In some cases, it is able to repair damaged image structure, such as the periodic mesh structure in row three, while in others it ha… view at source ↗
read the original abstract

Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) frames, relying on temporal redundancy and spatial coherence across the burst. While conventional methods achieve impressive results, they often struggle with complex textures and oversmoothing. Diffusion models, particularly those pretrained on high-quality data, have shown remarkable capability in generating realistic details for image and video super-resolution. However, their potential remains largely under-explored in BISR, where existing approaches typically rely on task-specific diffusion models trained from scratch and operate on single-frame reconstructions. In this work, we propose BurstGP, a novel diffusion-based solution for BISR, which leverages generative priors of recent foundation models to overcome these issues. In particular, we build a multiframe-aware diffusion model on top of a conventional BISR approach, which boosts image quality with minimal loss to fidelity. Further, we introduce (i) a novel degradation-aware conditioning mechanism, which controls synthesis of fine details based on the estimated degradation in the input, and (ii) a robust sRGB-to-lRGB inverter, enabling us to utilize generative multiframe (video) sRGB priors, while operating with raw input and lRGB output images. Empirically, we demonstrate that BurstGP outperforms the existing state of the art, both quantitatively (especially with respect to perceptual metrics, including MUSIQ and LPIPS) and qualitatively. In particular, our proposed method excels at recovering richer textures and finer structural details, highlighting the potential of video priors for BISR over traditional methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces BurstGP, a diffusion-based approach for raw burst image super-resolution (BISR) that augments a conventional BISR pipeline with a multiframe-aware diffusion model leveraging pretrained video generative priors. It incorporates a degradation-aware conditioning mechanism to control detail synthesis based on input degradation estimates and a robust sRGB-to-lRGB inverter to bridge video sRGB priors with raw Bayer burst inputs and linear RGB outputs. The central empirical claim is that this yields state-of-the-art performance, particularly on perceptual metrics such as MUSIQ and LPIPS, with superior recovery of textures and structural details compared to prior BISR methods.

Significance. If the transfer of video diffusion priors via the proposed conditioning and inverter can be shown to add realistic detail without introducing unfaithful content or fidelity loss relative to the raw measurements, the work would demonstrate a practical route for adapting large-scale generative models to raw burst tasks. This could shift BISR from purely task-specific training toward reuse of foundation-model priors, with potential gains in perceptual quality where traditional aggregation methods oversmooth.

major comments (3)
  1. [Abstract] The central claim that the sRGB-to-lRGB inverter and multiframe-aware conditioning enable 'minimal loss to fidelity' while transferring video priors (Abstract) is load-bearing for the superiority argument, yet the manuscript provides no quantitative fidelity analysis (e.g., PSNR/SSIM on the raw measurements before/after inversion or diffusion sampling) or ablation isolating the inverter's contribution. Without this, perceptual gains on MUSIQ/LPIPS could reflect hallucinated content rather than faithful super-resolution.
  2. [Abstract] The degradation-aware conditioning is described as controlling synthesis 'based on the estimated degradation in the input,' but no explicit formulation, network diagram, or training objective is supplied that shows how the conditioning signal is injected into the diffusion process or how it constrains the generative prior to the burst's temporal redundancy and noise statistics.
  3. [Abstract] The empirical superiority claim (outperforming SOTA on perceptual metrics) is stated without reference to the experimental protocol, baseline implementations, dataset splits, or error bars; the absence of these details in the text leaves the quantitative results unverifiable and prevents assessment of whether the gains are statistically significant or consistent across burst lengths and degradation levels.
minor comments (2)
  1. [Abstract] The abstract refers to 'recent foundation models' and 'video priors' without citing the specific pretrained models or their training data characteristics (e.g., noise model, color space), which would help readers evaluate the domain gap addressed by the inverter.
  2. [Abstract] Notation for the output space (lRGB) is introduced without an explicit definition or comparison to standard linear RGB processing pipelines used in raw-image literature.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below, providing clarifications from the full manuscript where applicable and outlining specific revisions to enhance verifiability and rigor.

read point-by-point responses

  1. Referee: [Abstract] The central claim that the sRGB-to-lRGB inverter and multiframe-aware conditioning enable 'minimal loss to fidelity' while transferring video priors (Abstract) is load-bearing for the superiority argument, yet the manuscript provides no quantitative fidelity analysis (e.g., PSNR/SSIM on the raw measurements before/after inversion or diffusion sampling) or ablation isolating the inverter's contribution. Without this, perceptual gains on MUSIQ/LPIPS could reflect hallucinated content rather than faithful super-resolution.

    Authors: We agree that explicit quantitative fidelity analysis strengthens the central claim. The manuscript reports PSNR and SSIM on final outputs versus ground truth, but does not isolate the inverter step or provide before/after inversion metrics on raw measurements. We will add these in the revision: (i) PSNR/SSIM computed on raw Bayer data before and after the sRGB-to-lRGB inversion, (ii) an ablation removing the inverter to quantify its contribution, and (iii) additional checks (e.g., LPIPS on inverted intermediates) to rule out hallucination. These additions will directly address whether perceptual gains preserve fidelity to the input measurements. revision: yes

  2. Referee: [Abstract] The degradation-aware conditioning is described as controlling synthesis 'based on the estimated degradation in the input,' but no explicit formulation, network diagram, or training objective is supplied that shows how the conditioning signal is injected into the diffusion process or how it constrains the generative prior to the burst's temporal redundancy and noise statistics.

    Authors: The abstract is necessarily concise; the full manuscript (Section 3.2) defines the degradation estimator as a lightweight CNN that outputs per-frame noise and blur scalars from the burst stack, which are then concatenated as extra channels to the diffusion U-Net's input and timestep embedding. The training objective augments the standard diffusion loss with a degradation-consistency term that penalizes deviation from the estimated noise statistics. We will expand this in the revision by adding the precise equations, a network diagram illustrating the injection points, and a description of how the conditioning preserves temporal redundancy across frames. This will make the mechanism fully explicit and reproducible. revision: yes

  3. Referee: [Abstract] The empirical superiority claim (outperforming SOTA on perceptual metrics) is stated without reference to the experimental protocol, baseline implementations, dataset splits, or error bars; the absence of these details in the text leaves the quantitative results unverifiable and prevents assessment of whether the gains are statistically significant or consistent across burst lengths and degradation levels.

    Authors: Section 4 of the manuscript specifies the datasets (e.g., synthetic and real burst splits with exact train/val/test ratios), baseline re-implementations (using official code where available, with our training details), and evaluation protocol (including MUSIQ, LPIPS, and PSNR). However, we acknowledge the lack of error bars and statistical tests in the main text. We will revise by adding standard deviations across multiple seeds and bursts to all tables, reporting p-values for key comparisons, and including a supplementary analysis of performance consistency across burst lengths (2–8 frames) and degradation levels. These changes will allow direct verification of statistical significance and robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity; method augments external pretrained diffusion priors with novel conditioning and inverter, validated empirically.

full rationale

The paper introduces BurstGP by stacking a multiframe-aware diffusion model atop a conventional BISR pipeline, using a degradation-aware conditioning mechanism and an sRGB-to-lRGB inverter to adapt video diffusion priors to raw burst inputs. These components are presented as engineering contributions whose effectiveness is demonstrated through quantitative comparisons (MUSIQ, LPIPS) and qualitative results on standard benchmarks. No equations or claims in the provided description reduce a prediction or uniqueness result to a fitted parameter or self-citation that is defined by the target outcome itself; the central performance gains are attributed to the external foundation models and the proposed adapters rather than to any tautological redefinition. The derivation chain therefore remains self-contained against external data and pretrained weights.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the transferability of pretrained diffusion priors to the raw burst domain. No new physical entities are postulated. The approach inherits standard assumptions from diffusion literature and conventional BISR pipelines.

axioms (1)
  • domain assumption Pretrained diffusion models trained on high-quality sRGB data encode useful generative priors that can be conditioned for low-level restoration tasks.
    Invoked when the paper states that foundation models overcome oversmoothing issues in BISR.

pith-pipeline@v0.9.0 · 5638 in / 1273 out tokens · 76831 ms · 2026-05-08T06:48:15.793594+00:00 · methodology

discussion (0)

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