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REVIEW 4 major objections 6 minor 29 references

ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read ExpRDiff uses smartphone short-exposure frames to guide a diffusion model in removing local motion blur, reporting better perceptual quality than state-of-the-art deblurring methods on both a synthetic benchmark and a new real-world…

desk verdict A sensible engineering recipe for local motion deblurring with short-exposure guidance, but the evaluation needs distortion metrics, a diffusion baseline, and a clearer dataset description to support the headline gains. read the letter →

arxiv 2412.09193 v1 pith:XBDCVTQF submitted 2024-12-12 cs.CV

Zhongbao Yang , Jiangxin Dong , Jinhui Tang , Jinshan Pan This is my paper · ORCID
classification cs.CV
keywords localmotiondeblurringshort-exposureguidancediffusionmodelblurdetectionguidedimagefilteringSDXLperceptualqualitysmartphonephotography
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 introduces ExpRDiff, a network for removing blur caused by moving objects in photos. It claims that a short-exposure frame of the same scene, which captures the moving object sharply, can be turned into reliable guidance for restoration by first detecting blurry regions, applying a masked guided filter, and then feeding fused short-exposure and blur features into an SDXL diffusion model. The central bet is that this guidance lets a diffusion prior restore realistic detail in heavily blurred areas without smoothing the clear background. If correct, the recipe gives smartphone cameras a practical way to turn a quick capture into a sharp photo of a moving scene.

What carries the argument

The load-bearing object is the short-exposure image itself, treated as a structural prior for the moving objects. ExpRDiff couples three uses of it: a context-based local blur detection module that classifies image patches with a Gumbel-Softmax confidence map; a blurry-aware guided image restoration module that solves a masked guided-filter optimization so that only blurry regions adopt the short-exposure structure while clear background stays as captured; and a short-exposure guided diffusion model whose ExpBFusion module injects mask-weighted short-exposure and blur features into the SDXL U-Net through attention. The mask from the detector also eliminates boundary artifacts left by the guided filter.

What would settle it

A concrete test: take real paired long/short-exposure captures from LMBD, deliberately misalign the short-exposure image by a few pixels, and measure the perceptual scores of the restored image. If the scores stay at the reported level, the short-exposure guidance is not actually carrying the result; if they drop sharply, the premise that aligned sharp short-exposure structure drives the improvement is confirmed. A second check: replace the synthetic short-exposure images used in ReLoBlur training with real short-exposure captures and see whether the gain over the synthetic-trained model disappears.

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Extended reading notes

Core claim

The central claim is that short-exposure-guided diffusion, not merely having the extra reference image, is what makes local motion deblurring work well. Given a long-exposure blurry image and a short-exposure image of the same scene, ExpRDiff first learns a context-based blur detector that marks blurry patches by aggregating patch-level confidence values, then applies a blur-aware masked guided filter that transfers sharp structure from the short-exposure frame only inside the detected blurry regions while leaving clear background untouched. A short-exposure guided diffusion model then refines the result: an ExpBFusion module injects mask-weighted features from both the short-exposure image and the detected blurry regions into the SDXL U-Net through attention, which the paper shows also removes boundary artifacts left by the guided filter. On the ReLoBlur synthetic benchmark and the newly captured LMBD dataset, ExpRDiff reports lower NIQE and higher CLIPIQA+ and MUSIQ-PAQ2PIQ than global deblurring baselines, local deblurring methods, and the same baselines given the short-exposure image as an extra input.

Load-bearing premise

During training, short-exposure images are simulated by lowering brightness in HSV space and adding random noise to sharp frames (Section 4.1); if real short-exposure captures are misaligned with the long-exposure frame, have a different noise profile, or miss the moving object, the guidance can inject wrong structure and the deblurring gains shrink.

Editorial extensions

If this is right

  • Smartphone burst or multi-camera captures become a practical input: a short-exposure frame of a moving object can serve as guidance, fitting the workflow of cameras that already grab extra frames.
  • Because the mask keeps clear background untouched, users avoid the over-smoothing that global deblurring applies to static regions.
  • The diffusion prior can hallucinate realistic texture in heavily blurred regions, so the restored image looks natural even where the short-exposure frame itself is noisy or underexposed.
  • The approach inherits the computational cost of an SDXL diffusion backbone, so deployment requires a diffusion-capable runtime rather than the lightweight CNN deblurring models it outperforms.
  • Training on synthesized short-exposure images together with a small real dataset transfers to real-world local blur, as demonstrated on the LMBD dataset.

Reading between the lines

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

  • The synthesis of short-exposure images by lowering HSV brightness and adding random noise is a cheap proxy for real short-exposure physics; a natural extension is to learn the proxy from real paired captures or to model the exposure-difference transfer function explicitly.
  • Because the blur mask is patch-level and upsampled with nearest-neighbor interpolation, fine object boundaries are approximated; a boundary-refinement module or instance-aware segmentation could sharpen the mask and further improve edge detail.
  • The reported metrics (NIQE, CLIPIQA+, MUSIQ-PAQ2PIQ) align with the paper's claim about visual quality, but a side-by-side human preference study would test whether the perceptual gains are noticeable to viewers.
  • The same guided-fusion recipe may transfer to other local degradations where a short-exposure or differently-exposed frame exists, such as local glare, reflections, or rolling-shutter artifacts.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes ExpRDiff, a three-module pipeline for local motion deblurring: a context-based local blur detection module that classifies non-overlapping image patches with Gumbel-Softmax; a blur-aware guided image restoration step that applies a masked guided filter using the detected confidence map and a short-exposure image R; and a short-exposure guided diffusion model built on SDXL, in which multi-scale features from R and the blur map are fused into the diffusion U-Net features via an attention module (ExpBFusion). The method is trained on ReLoBlur with synthetic short-exposure images generated by darkening sharp frames in HSV space and adding noise, and on a newly captured real dataset, LMBD. Quantitative results are reported on ReLoBlur and LMBD using only the no-reference metrics NIQE, CLIPIQA+, and MUSIQ-PAQ2PIQ, with ablations on LMBD.

Significance. If the claims are substantiated, the paper makes a useful practical contribution: the idea of exploiting smartphone short-exposure captures as structural guidance for local motion deblurring is well motivated, and the comparison includes retrained baselines with and without the short-exposure image as additional input, which is a fair control. The ablations show each proposed module contributes at least some positive effect, and a code link is provided. I also do not see a circularity problem: the short-exposure image is a genuine extra input rather than a reparametrization of the output. However, the evidence is currently insufficient for the headline claim. All comparisons rely on no-reference metrics, the real-world dataset has no ground-truth sharp image, and the synthetic-to-real transfer of short-exposure images is not validated. The significance is therefore conditional on additional evaluation.

major comments (4)
  1. [Section 4.1, Tables 1-2] The central comparison is based exclusively on the no-reference metrics NIQE, CLIPIQA+, and MUSIQ-PAQ2PIQ, even though the ReLoBlur synthetic dataset provides ground-truth sharp images. No PSNR, SSIM, LPIPS, or any distortion metric is reported, and no user study is conducted. Since a generative diffusion prior can improve no-reference quality scores while hallucinating content that does not match the scene, the claim that ExpRDiff 'performs favorably against state-of-the-art methods' is not yet established. Please report reference-based metrics on ReLoBlur and a perceptual user study on LMBD, and ideally include a diffusion-based restoration baseline in the comparison.
  2. [Section 4.1 and Eqs. (4)-(6)] The key premise of the method is that the short-exposure image R is a reliable structural reference, but during training R is synthesized by converting sharp images to HSV, reducing brightness, and adding random noise, while at inference R is a real smartphone capture. The manuscript reports no alignment preprocessing for LMBD, no analysis of the distribution shift between synthetic and real R, and no validation that the real R contains the moving object in the same pose. If R is misaligned or has a different noise/color distribution, the masked guided filter in Eq. (4) can copy incorrect structure into blurry regions, so the large NIQE gains in Table 2 could come from the SDXL prior rather than from R. The manuscript repeatedly refers to a supplemental material that is not included in the arXiv submission, so the LMBD capture protocol cannot be checked. Please quantify alignment error or provide a real-pair study, for example by comparing against a deliberately misaligned R or a synthetic R.
  3. [Section 5, Tables 3-5] The ablations use only CLIPIQA+ and MUSIQ-PAQ2PIQ on LMBD, and the reported differences are small. For example, in Table 5, 'w/ M' versus 'w/ M^gt' differs by 0.15 in MUSIQ-PAQ2PIQ and 0.004 in CLIPIQA+; in Table 4, 'w/ F & w/o M' versus 'w/ F' differs by 0.44 in MUSIQ-PAQ2PIQ. No standard deviations, confidence intervals, or significance tests are reported, so the claim that each designed module contributes is not quantitatively supported. Please provide multiple runs or significance testing, and include at least one reference-based metric or a user study for the ablations.
  4. [Section 4.1 and Section 5] The training and evaluation of the LMBD model are underspecified. The text states that LMBD contains 10,380 images for training and 209 for evaluation, but it does not describe how ground-truth masks M^gt are obtained for real LMBD images, even though those masks are needed for the detection loss in Eq. (2) and for the 'w/ M^gt' ablation in Table 5. It also does not clearly state whether the Table 2 baselines are trained on ReLoBlur or LMBD and with which hyperparameters. Please clarify the dataset construction and the exact training/evaluation protocol for each table.
minor comments (6)
  1. [Section 4.2] The sentence after Table 1 says the proposed method achieves 'higher NIQE' but NIQE is a lower-is-better metric; the text should read 'lower NIQE'.
  2. [Section 3.1 and Figure 2] Notation is inconsistent: R denotes both the short-exposure image and the folding operation in Eq. (3), and D denotes the detection network in Eq. (1) but is described as the restoration module in the Figure 2 caption. Please disambiguate these symbols.
  3. [Sections 3.1-3.2] The Gumbel-Softmax temperature τ, the binarization threshold for M, and the regularization parameter ϵ are never given values, and the phrase 'a threshold based on the default value of the Gumbel-Softmax distribution' is not well-defined. State the exact values and any annealing schedule.
  4. [Section 4.1] The sentence 'We employ the loss function [22] to regularize our network' does not state the loss; the objective should be written out so the training procedure is self-contained.
  5. [Section 3.3] The paper claims that the short-exposure guided diffusion model demonstrates its 'effectiveness and efficiency', but no runtime, FLOPs, parameter count, or memory comparison is reported anywhere in the manuscript.
  6. [Figures and Eq. (6)] There are typos such as '2st patch' in Figures 1 and 6, and the formula for b_k in Eq. (6) is typeset without a visible division by its denominator. Please fix these presentation issues.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ExpRDiff's short-exposure guidance is a genuine extra input and the method is trained end-to-end on external benchmarks.

full rationale

The paper's derivation chain is self-contained: the proposed method is a supervised end-to-end network trained on paired blurry/short-exposure/sharp data, and the short-exposure image is a genuine additional input rather than a reparametrization of the output. The blur-aware guided restoration (Eqs. 4-6) is a masked variant of guided image filtering, solving for linear coefficients from the blur input B and short-exposure input R; the output H is not derived from the target sharp image by construction. The diffusion model uses an externally pretrained SDXL prior with features fused from R and the blur mask M, again using extra inputs. Ablations compare against ground-truth masks and against variants without the short-exposure guidance, so the contributions are empirically isolated rather than assumed. The only self-citation in the paper, reference [7], appears in related work and is not load-bearing for any claimed result. Concerns about synthetic-to-real domain shift for short-exposure images are potential correctness risks, not circularity, and do not warrant a circularity score.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a small number of hand-chosen hyperparameters (temperature, regularization, threshold, short-exposure synthesis settings) and on domain assumptions about the fidelity of the short-exposure reference and the validity of no-reference quality metrics. There are no invented physical entities or new formal axioms beyond standard deep-learning practice.

free parameters (4)
  • Gumbel-Softmax temperature tau = not reported
    Controls the smoothness of the blur-confidence distribution in Eq. (1); no value or schedule is given.
  • Guided-filter regularization epsilon = not reported
    Weights the a_k penalty in Eq. (5); hand-chosen, no value stated.
  • Mask binarization threshold = not reported
    Section 3.2 says a threshold based on the default of Gumbel-Softmax binarizes M; the exact value is unspecified.
  • Short-exposure synthesis brightness/noise levels = not reported
    Section 4.1 generates short-exposure images by reducing brightness in HSV and adding random noise; the reduction ratio and noise level are not given, and the model is sensitive to them.
assumptions (5)
  • domain assumption The short-exposure image contains sharp, aligned structures of the moving objects in the blurred regions.
    Central to the guided restoration and diffusion conditioning (Sections 3.2 and 3.3); if false (e.g., residual motion blur or parallax), the guidance injects wrong structure.
  • domain assumption Synthetic short-exposure images (HSV brightness reduction plus random noise on sharp frames) approximate real smartphone short-exposure frames well enough for training.
    Section 4.1 constructs training pairs this way; the method's real-world performance on LMBD depends on this transfer.
  • domain assumption No-reference metrics (NIQE, CLIPIQA+, MUSIQ-PAQ2PIQ) are the appropriate measures of deblurring quality, and PSNR/SSIM can be omitted.
    Section 4.1 cites the perception-distortion tradeoff to justify this choice; this is contested and is the main basis for the claimed improvements.
  • domain assumption The ReLoBlur and LMBD training/test splits are representative and the baselines were trained under comparable budgets.
    Fair comparison claims in Section 4.2 rely on this; retraining details are not fully reported.
  • standard math Gumbel-Softmax provides a differentiable approximation to sampling from the confidence distribution.
    Used in Eq. (1) for end-to-end training of the blur detector; standard result from [6].

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Cite this review

Pith. "Pith review of ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring." pith.science (2026). https://pith.science/paper/XBDCVTQF

@misc{pith2026241209193,
  author       = {Pith},
  title        = {Pith review of: ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XBDCVTQF}},
  note         = {Machine review of arXiv:2412.09193}
}
read the original abstract

Removing blur caused by moving objects is challenging, as the moving objects are usually significantly blurry while the static background remains clear. Existing methods that rely on local blur detection often suffer from inaccuracies and cannot generate satisfactory results when focusing solely on blurred regions. To overcome these problems, we first design a context-based local blur detection module that incorporates additional contextual information to improve the identification of blurry regions. Considering that modern smartphones are equipped with cameras capable of providing short-exposure images, we develop a blur-aware guided image restoration method that utilizes sharp structural details from short-exposure images, facilitating accurate reconstruction of heavily blurred regions. Furthermore, to restore images realistically and visually-pleasant, we develop a short-exposure guided diffusion model that explores useful features from short-exposure images and blurred regions to better constrain the diffusion process. Finally, we formulate the above components into a simple yet effective network, named ExpRDiff. Experimental results show that ExpRDiff performs favorably against state-of-the-art methods.

Figures

Figures reproduced from arXiv: 2412.09193 by the authors.

Figure 1
Figure 1. Deblurring images with real-world local motion blur. The left side shows examples of a long-exposure photo (top) and a short [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. An overview of the proposed method ExpRDiff. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An overview of the proposed context-based local blur detection module to detect the blur regions of the long-exposure blur [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Details of the proposed ExpBFusion module. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Illustration of guided image filtering [ [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Deblurred results on the ReLoblur synthetic dataset [ [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Deblurred results on the LMBD dataset. The deblurred results in (c)-(g) still contain significant blur effects. The proposed [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: Effectiveness of the proposed ExpBFusion on image [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: Effectiveness of the proposed blurry-aware guided im [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 11
Figure 11. Figure 11: Effectiveness of the proposed short-exposure guided [PITH_FULL_IMAGE:figures/full_fig_p008_11.png]
Figure 12
Figure 12. Figure 12: Effectiveness of the context-based local blur detection [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]

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