Title: DeblurDiff: Real-Word Image Deblurring with Generative Diffusion Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image deblurring
Abstract: Diffusion models have achieved significant progress in image generation and the pre-trained Stable Diffusion (SD) models are helpful for image deblurring by providing clear image priors. However, directly using a blurry image or a pre-deblurred one as a conditional control for SD will either hinder accurate structure extraction or make the results overly dependent on the deblurring network. In this work, we propose a Latent Kernel Prediction Network (LKPN) to achieve robust real-world image deblurring. Specifically, we co-train the LKPN in the latent space with conditional diffusion. The LKPN learns a spatially variant kernel to guide the restoration of sharp images in the latent space. By applying element-wise adaptive convolution (EAC), the learned kernel is utilized to adaptively process the blurry feature, effectively preserving the information of the blurry input. This process thereby more effectively guides the generative process of SD, enhancing both the deblurring efficacy and the quality of detail reconstruction. Moreover, the results at each diffusion step are utilized to iteratively estimate the kernels in LKPN to better restore the sharp latent by EAC in the subsequent step. This iterative refinement enhances the accuracy and robustness of the deblurring process. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art image deblurring methods on both benchmark and real-world images.


Peer reviews:
comments: SUMMARY:
This paper propose a novel diffusion-based image deblurring architecture that combining both supervised training and pretrained generative priors, which achieves the SOTA performance on multiple dataset with regard to the visual quality.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The method outperforms other SOTA methods on non-reference based metrics.
2. A novel kernel predictor to map the low-quality latent feature to high-quality, avoid the artifacts and blur on the condition.

Weaknesses
1. Lacks the quantitative evaluation of ablation study.
2. In the Figure 13 of the supp, I think "Latent Kernel Prediction Network" should change a name.
3. If all methods are retrained on the proposed dataset, then it does not make sense to me that the proposed method performs worse on PSNR and SSIM, since there is a L2 loss during the training of this method.

QUESTIONS:
1. Authors mentioned "All the aforementioned retrained methods are trained and tested using the publicly released codes and models of the competing methods". Did you just retrain the DiffBIR or all compared methods. If you retrained all compared methods, then why did the proposed method performs worse on synthetic data. The proposed method also used L2 supervision between ground-truth and output, same as other method.

2. Some related method [https://arxiv.org/pdf/2308.02947] estimate the adaptive kernels on the original image, have you tried this idea instead of on the latent space? And advantage of estimate the kernel in the latent space?

LIMITATIONS:
1. Long inference time makes it inapplicable for real time scenarios
2. The information loss of VAE is inevitable, which may lead to a slight blur on the final result.
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-11

comments: SUMMARY:
This paper presents DeblurDiff, a novel real-world image deblurring framework that integrates a Latent Kernel Prediction Network (LKPN) with pre-trained Stable Diffusion (SD) models. The LKPN predicts pixel-wise adaptive kernels to refine blurry latent representations via Element-wise Adaptive Convolution (EAC), providing improved structural guidance for the diffusion process. Extensive experiments demonstrate superior perceptual quality over state-of-the-art CNN-, Transformer-, and diffusion-based deblurring methods.

STRENGTHS AND WEAKNESSES:
+ Strong design combining SD priors with an adaptive kernel estimation strategy; ablation studies (Table 2) and multiple benchmarks validate the method.
+ The paper is generally well-written with clear motivation, figures, and architectural illustrations (Fig.3).
+ Shows practical potential by addressing limitations of existing degradation-removal pipelines.

- The improvement in reference-based metrics (PSNR, SSIM) over prior methods is modest, despite substantial complexity.
- Limited discussion on computational overhead of iterative kernel refinement during inference.

QUESTIONS:
Please refer to the weakness part.

LIMITATIONS:
Yes. The authors mention limitations regarding SD model versions and runtime efficiency.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nDirectly using a blurry image or pre-deblurred one as a conditional control for Stable Diffusion will either hinder accurate structure extraction or make the results overly dependent on the deblurring network. In this work, author proposes a Latent Kernel Prediction Network (LKPN) to achieve robust real world image deblurring.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well written.\n\nQUESTIONS:\n1.The compared methods are still too outdated — DiffBIR was released in 2023. I hope the authors can compare with more recent SOTA methods to truly demonstrate the effectiveness of their approach.\n\n2.The author's method is compared with several others in Table 1. From what I see, these methods were all released in 2023. Even so, I find that the performance of the author's method is still not SOTA. I'm also confused because the author's PSNR, SSIM, and LPIPS scores are not as good as those of Hi-Diff, MUSIQ, NIQE, CLIP-IQA, or even PASD (also from 2023). So, what justifies the claim that your method is advanced?\n\n3.Please provide a comparison of all methods in Table 1 in terms of the number of parameters and inference time.\n\n4.The NIQE and MANIQA metrics used in Table 2 are no-reference quality assessment indicators, and they exhibit large fluctuations. Furthermore, they cannot accurately evaluate visual quality. Metrics like PSNR, SSIM, and LPIPS would be more appropriate here.\n\n5.The novelty of the paper is not very strong. Degradation estimation in Latent Diffusion Models (LDM) has already been explored by many image restoration methods, such as:\n\n[1] DiffIR: Efficient diffusion model for image restoration\n\n[2] DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion\n\n[3] Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents DeblurDiff, a new diffusion based model for real-world image deblurring. The key contribution is alternating diffusion of two entities: latent kernel and latent image feature. The Latent Kernel Prediction Network (LKPN) predicts spatially variant deblurring kernels within the latent space and it is trained in conjunction with a conditional diffusion model. These kernels are then applied to the latent representation of the blurry image using Element-wise Adaptive Convolution (EAC), producing a cleaner conditional input for the diffusion process. This iterative process is used to refine the kernel prediction and latent image prediction for the subsequent step. The model achieves state-of-the-art results, especially in terms of perceptual quality, across both synthetic and real-world deblurring datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nUsing diffusion based Kernel Prediction in the latent domain to iteratively refine the input for the diffusion based restoration model is a clever idea. It tackles a prevalent problem of diffusion based restoration models - how you provide conditioning without losing important detail or introducing artifacts.\n\nThe perceptual quality of results shown is quite impressive. Table 1 shows clear improvement on metrics like MUSIQ and CLIP-IQA. The qualitative results reflect this very well too, by achieving a tough balance between sharpness and fidelity.\n\nThe proposed large scale dataset (if released) is also an important contribution to the future development in the deblurring community. Although it is not completely new (built upon existing datasets such as DVD, RSBlur etc.), its careful curation seems effective, as exemplified in the qualitative results.\n\nThe details and insights (specially on effect of diffusion model ablations and datasets) provided in the supplementary material are quite informative!\n\n\n\nWeaknesses:\n\nContributions are lacking in terms of novel architectural components or a more profound theoretical insight. While the overall framework is novel, the individual components are not fundamentally so. The paper presents a clever engineering solution by employing relatively known ideas (such as Deep kernel prediction and Element-wise Adaptive Convolution) in a diffusion based formulation. Pixel-adaptive Filter estimation and element wise convolution (in feature domain) for motion deblurring has been proposed and successfully used in a prior research eg. “Spatio-Temporal Filter Adaptive Network for Video Deblurring (ICCV 2019)”. Similarly, joint sharp image and blur kernel estimation (in pixel domain) through diffusion has been proposed in an existing paper: “Parallel Diffusion Models of Operator and Image for Blind Inverse Problems (CVPR 2023)”. Such literature should be included in related works and experiments/analysis. \n\n\nThe paper is primarily empirical and there is a lack of theoretical analysis of why the proposed iterative refinement process should converge or why it is superior to other potential approaches.\n\n\nThe filtered blurred images (obtained by applying the estimated filters on the blurred image latent and passing it to the decoder to obtain an image) are not shown in the paper. Since learning accurate deblurring filters is at the core of the paper, it is important to demonstrate that through visual examples. \n\n\nThe proposed approach’s parameter count and FLOPs (which are crucial for practical applications) have not been discussed or compared with state-of-the-art deblurring methods. Since the proposed method involves two diffusion processes with multiple sampling steps, it would be interesting to compare its overall speed with the best existing methods.\n\n\nThe qualitative results in Figs. do not contain results of existing methods retrained on new dataset. It is difficult to disentangle the effect of model design and new dataset. It would be helpful to know which of these changes contribute more to the significant qualitative improvements over the existing methods. \n\n\nThe proposed method’s key metric scores (MUSIQ, CLIP-IQA) are inferior to the existing approach PASD on real images (shown in Table). Does this indicate overfitting of the model on training data? \n\nThe main paper does not provide enough details and experiments on the LKPN architecture. The architecture presented in Fig. 3 only shows a couple of layers while the actual LKPN (as mentioned in the supplementary material) is a UNet with 8 encoder and decoder blocks containing a ResBlock and a self-attention layer. For reproducibility and a deeper understanding of the method, it would be beneficial to know and architectural ablations, and know more details such as specific layers, number of channels, parameters, etc.\n\n\nSince paper implementation is not provided, extensive qualitative results on key benchmarks should be provided for better assessment and future comparisons.\n\nQUESTIONS:\nOperations such as attention have become the new norm and they have essentially replaced convolution operations in image restoration literature. Did the project explore any alternatives to convolution operation?\n\nI wonder if a DRM would also benefit the proposed approach. Intuitively, adding a predeblurred image (using a DRM) to the conditioning images could further boost the accuracy of the model. Since the denoiser of ControlNet is capable of accepting multiple latents as input (in concatenated format), maybe we could also supply the latents extracted from a predeblurred image This could further improve scores since exiting methods such as PASD using such predeblurred image are already doing well across benchmarks, specially on real images (Table 1)\n\nThe paper mentions that a pre-trained FFTformer is used as the DRM in DiffBIR. Kindly clarify whether or not the FFTformer used in DiffBIR is retrained on the proposed dataset. If not, the comparison with DiffBIR would not be completely fair.\n\nKindly also address the key weaknesses mentioned above.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-11"}