Title: Efficient RAW Image Deblurring with Adaptive Frequency Modulation
Venue: NeurIPS 2025
Accepted: yes
Keywords: frequency domain

image deblurring

raw image deblurring
Abstract: Image deblurring plays a crucial role in enhancing visual clarity across various applications. Although most deep learning approaches primarily focus on sRGB images, which inherently lose critical information during the image signal processing pipeline, RAW images, being unprocessed and linear, possess superior restoration potential but remain underexplored.  Deblurring RAW images presents unique challenges, particularly in handling frequency-dependent blur while maintaining computational efficiency. To address these issues, we propose Frequency Enhanced Network (FrENet), a framework specifically designed for RAW-to-RAW deblurring that operates directly in the frequency domain. We introduce a novel Adaptive Frequency Positional Modulation module, which dynamically adjusts frequency components according to their spectral positions, thereby enabling precise control over the deblurring process. Additionally, frequency domain skip connections are adopted to further preserve high-frequency details. Experimental results demonstrate that FrENet surpasses state-of-the-art deblurring methods in RAW image deblurring, achieving significantly better restoration quality while maintaining high efficiency in terms of reduced MACs. Furthermore, FrENet's adaptability enables it to be extended to sRGB images, where it delivers comparable or superior performance compared to methods specifically designed for sRGB data. The source code will be publicly available.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, an efficient RAW image deblurring network, FrENet, is proposed, which combines spatial and frequency domain modeling to enhance the quality and efficiency of image reconstruction. The authors introduce a novel frequency-domain positional modulation module, Adaptive Frequency Positional Modulation (AFPM), for the characteristics of frequency-dependent blurring in RAW data, which is able to adaptively adjust the features according to the spectral position and effectively recover the high-frequency details. The network adopts the U-Net architecture and introduces a frequency-domain skip connection to further convey multi-scale spectral information. Experiments are conducted on multiple datasets, and FrENet outperforms multiple SOTA methods in terms of PSNR, SSIM, and MACs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The AFPM module enables dynamic modulation of frequency-domain position sensing and is an efficient extension to frequency modeling.\n2. The model significantly reduces computational costs while maintaining high image quality.\n3. Multi-layer visualizations of frequency-domain feature changes and modulation kernels are provided, demonstrating the interpretive nature of the model's learning capabilities.\n\nWeaknesses:\n1. Comparisons of purely spatial versions with frequency domain enhancement paths removed are not shown, making it difficult to explicitly assess the independent contribution of frequency domain designs.\n2. Despite the low MACs, the actual runtime cost of FFT/IFFT is not quantified across image resolutions.\n\nQUESTIONS:\n1. The current work introduces frequency-domain enhancement modules (AFPM and frequency-domain skip connection) based on the NAFNet framework, but the paper does not show a “pure spatial-domain” version with the frequency-domain paths removed as a control. It is recommended that the authors add the following control experiments: \n1) Performance of FrENet with all frequency domain operations (including AFPM and frequency domain skip connections) completely removed, i.e., the spatially enhanced version of the model; \n2) A fair comparison with an equivalent number of parametric versions of NAFNet to rule out the possibility that the performance improvement is mainly due to the increased capacity of the model rather than to structural innovations.\n2. AFPM is a learnable modulation module based on frequency-domain position sensing, which is highly versatile. However, the current work has only been validated in the de-blurring task of RAW images. It is recommended that the authors further explore and try to apply FrENet to other typical RAW image restoration tasks, such as denoising and demosaicing, to demonstrate the adaptability and scalability of the method.\n3. Although FrENet demonstrates low MACs, its FFT/IFFT operations during inference may introduce additional performance bottlenecks under high-resolution images due to its reliance on frequency domain modeling. It is recommended that the authors provide statistical results of the actual inference time comparisons with other mainstream methods.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FrENet, a novel framework for efficient RAW image deblurring that operates in the frequency domain. The core contributions are twofold: a new Adaptive Frequency Positional Modulation (AFPM) module that dynamically adjusts frequency components based on their spectral position, and the use of frequency domain skip connections to better preserve high-frequency details. The proposed method achieves state-of-the-art restoration quality on RAW deblurring tasks with significantly improved computational efficiency, and also shows strong performance when extended to the sRGB domain.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed Adaptive Frequency Positional Modulation (AFPM) module is a key contribution. It deviates from uniform filtering by learning to modulate frequency bands based on their spectral position. This mechanism enables more targeted detail restoration and is supported by restoration performance and visualization. The AFPM may be extended to other restoration tasks. \n2. The architectural design is concise, and thus the proposed method is efficient, which is crucial for raw domain restoration applications. \n3. The paper provides comprehensive experimental results to support its claims. The results show strong performance in both RAW and sRGB deblurring tasks, and the computational efficiency is also validated.\n\nWeakness:\n1. The paper's explanation for its content-agnostic AFPM mechanism is insufficient. The module generates modulation kernels based solely on a frequency patch's spectral position, yet the paper does not offer a explanation for why positional information alone is a sufficient guide for deblurring. \n2. The use of SCA in the frequency domain needs more discussion. This mechanism was designed for spatial data, and its core operation—global average pooling—treats frequency components as if they were interchangeable pixels. More explanations are needed for the use of SCA in frequency domain.\n4. The AFPM module operates on non-overlapping patches of the frequency map. This hard partitioning could potentially introduce boundary artifacts or discontinuities in the modulation process between adjacent frequency patches. Some experiments are needed to disucss this issue.\n\nQUESTIONS:\nSee the points in Weakness.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of image deblurring in the RAW image domain, which remains relatively underexplored compared to sRGB image deblurring. The authors propose a Frequency Enhanced Network (FrENet) and introduces an Adaptive Frequency Positional Modulation (AFPM) module to dynamically adjust frequency components. Extensive experiments are conducted on both raw and RGB image dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n1. The paper highlights an important but underexplored area — RAW image deblurring — where existing methods mostly target sRGB images.\n2. The proposed Adaptive Frequency Positional Modulation (AFPM) introduces a novel way of modulating frequency components based on their spectral positions.\n\nWeaknesses：\n\n1. Lack of RAW-Specific Design Justification. While the authors emphasize the importance of RAW image deblurring, the proposed FrENet does not appear to incorporate any design specifically tailored to the unique characteristics of RAW images. Moreover, the network architecture closely resembles LoFormer [26], with the main difference being the addition of the AFPM module. The novelty and contributions thus need clearer justification.\n\n2. Limited Performance Gains. The improvement over existing methods is relatively minor. For example, in Table 2, the deblurring performance on sRGB datasets shows only marginal gains when comparing FrENet+ with LoFormer-L. Similarly, the reduction in computational cost is minimal — Table 1 reports 48.98M parameters for LoFormer-L and 48.38M for FrENet+, which is not a significant difference.\n3. Unfair Experimental Comparison. The baseline methods compared in Table 1 were originally designed for RGB image deblurring. A fair comparison would require converting the blurred RAW images to sRGB using a fixed ISP pipeline and then training these RGB deblurring methods (e.g., LoFormer, FFTFormer) on the resulting blurred sRGB images, before testing. This standardization is missing in the current experimental setup.\n4. The paper lacks a critical comparison between two key setups:\n(i) Performing deblurring directly on blurred RAW images and then applying a fixed ISP pipeline to obtain sRGB results,\nversus\n(ii) Converting blurred RAW images to sRGB via the ISP pipeline first, and then applying deblurring.\nThis comparison should be conducted using at least two different network architectures to validate generalization across setups.\n5. Incomplete Ablation Study. The ablation analysis is insufficient. There is no detailed breakdown of the contribution of the AFPM and SCA modules individually. Additionally, the paper does not provide runtime and inference cost analysis for each module separately, which is essential to understand their efficiency impact.\n\nQUESTIONS:\nThe paper tackles an important problem and proposes a conceptually interesting direction (frequency-domain RAW deblurring), but it lacks strong empirical support and sufficient novelty, especially given the limited justification for RAW-specific design and the unfair experimental comparisons. With clearer architectural motivation, fairer baselines, and more rigorous evaluations, this work could be improved for future submission.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents FrENet, an architecture for RAW image deblurring. The model avoids transformer operations and focuses on frequency domain processing using a module FACM. It contains a novel module AFPM to learn frequency position-dependent modulation using lightweight MLP-based kernel and bias generation. It achieves strong state-of-the-art performance on the Deblur-RAW dataset with notably improved MAC efficiency, and demonstrates design generalization to sRGB deblurring task.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nIt introduces a fairly novel module for frequency domain filtering wherein it divides the frequency spectrum into patches and applies a position-dependent modulation. Since motion blur is a frequency-specific degradation, modulating frequency-domain features based on their spectral position is a well-motivated idea and sounds more promising than uniform frequency domain filtering and memory-heavy spatial attention mechanisms. The idea is also empirically validated.\n\n\nThe overall design is effectively engineered, borrowing appropriate building blocks from competing models (FFN from Restormer, SCA from NAFNet), and adding frequency-domain skip connections. Ablation studies on frequency skip connections, local/global branches in AFPM, and patch granularity are informative.\n\n\nAlthough focusing on a relatively underexplored area of RAW-to-RAW deblurring, FrENet achieves an impressive trade-off between accuracy (PSNR/SSIM) and computational cost (MACs) when compared against the provided baselines, leading to notable gains over state-of-the-art.\n\nIn addition to the RAW domain deblurring, the design also generalizes moderately well to sRGB domain deblurring, with particularly good results on the RealBlur dataset. \n\n\n\n\n\nWeaknesses:\n\n\nAFPM module (paper's primary contribution) uses a simplistic and unjustified positional encoding. In frequency map, “using only the euclidean distance from the center, irrespective of the direction” is naive and suboptimal. Most real-world motion blur kernels are strongly directional in nature and hence blur properties are not rotationally invariant in the frequency domain. This design choice should be experimentally validated and compared with the standard non-isotropic encodings (e.g., using 2D sinusoidal/learned coordinates).\n\n\nSection 4.1 mentions that a sliding window strategy is employed to process full-resolution images, which is uncommon and not explained. Particularly, it’s worth noticing that the used sliding window size equals training patch size. This strongly indicates the model's rigidity to training resolution and lack of adaptability across image sizes and resolutions. This also implies slower inference. On a related note: it seems that fixing the division granularity to 8x8 also potentially contributes to limited resolution flexibility.\n\n\nComparisons are not extensive enough. Recent lightweight vision transformers for deblurring/restoration (e.g., StripFormer, Hybrid Attention Transformer) are potentially strong contenders in this category but are not included in the experimental comparisons. Also, few relevant models such as RawIR [10] and ELMFormer [23] are mentioned but not compared with. \n\n\nAlthough the MACs are informative, kindly provide comparison of actual inference time and GPU memory usage on high-res images. This is important because FFT/IFFT could be slow and memory-heavy.\n\n\nThere is no information about used hardware’s specs (number and type of GPUs), training duration etc which would be helpful in reproducing the experiments.\n\n\nIt’s not clear (but could be guessed) whether all the compared baselines were retrained on RAW dataset or only on sRGB and directly tested on RAW.\n\nQUESTIONS:\nKindly address the key limitations mentioned above, specially regarding training/testing hardware information, and inference speed/memory benchmarks on full or high-resolution RAW and sRGB inputs?\n\n\n\nCould you comment on the choice of proposed positional encoding in AFPM? Have you tested more principled alternatives e.g. learned or Fourier-based embeddings?\n\n\nThe proposed module performs channel concatenation of real and imaginary components of fourier features. Have the authors explored potential alternatives for processing fourier domain features eg. fourier convolution operations or magnitude-phase concatenation etc?\n\n\n\nCould you include the missing comparisons. Also clarify if all the baselines in RAW benchmark comparisons (e.g. LoFormer or Restormer) were retrained or adapted in any way for the RAW domain?\n\n\nCould you include any failure cases or limitations (with examples).\n\n\nIn Table 3, could you include the case of removing spatial skip connections too.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-13"}