Efficient RAW Image Deblurring with Adaptive Frequency Modulation
Authors: Wenlong Jiao
Binglong Li
Wei Shang
Ping Wang
Dongwei Ren
Venue: NeurIPS 2025 poster ()
Keywords: frequency domain
image deblurring
raw image deblurring
Primary area: deep_learning

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.

Efficient RAW Image Deblurring with Adaptive Frequency Modulation
Wenlong Jiao'
Binglong Li!
Wei Shang? Ping Wang'
Dongwei Ren
School of Mathematics, Tianjin University
2 School of Computer Science and Technology. Harbin Institute of Technology
$ School of Artificial Intelligence, Tianjin University
(wenlong, 1i Li_binglong, Lang-ping, rendw (tts)))@)) @tjje.jjieeeeeee.e .du.edd... csseesshag, rendongweihit @gmail com
Abstract
Image deblurring plays a crucial role in enhancing visual clarity across various applications. Although most deep learning approaches primarily focus sRGB images, which inherently lose critical information during the image signal pro- cessing pipeline, RAW images, being unprocessed and linear, possess superior restoration potential but remain underexplored. Deblurring RAW images presents unique challenges, particularly in handling frequency- dependenn blur while main- taining computational efficiency. To address these issues, we propose Frequency Enhanced Network (FrENet), 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 pre- cise control over the deblurring process. Additionally, frequency domain skip connections are adopted to further preserve righ-frreuency details. Experimental results demonstrate that FrENet surpasses wate-of-the-art deblurring methods RAW image deblurring, achieving significantly better restoration quality while maintaining high efficiency terms of reduced MACs. Furthermore, FrENet's adaptability enables it to be extended to sRGB images, where it delivers com- parable or superior performance compared to methods specifically designed for sRGB data. The source code and pre-trained models are publicly available at https: htops:///ithubee www//em///nnneeee com./VenlongJJao//FEtM..
Introduction
Image blur remains a pervasive challenge in computational photography, critically degrading visual quality and impeding downstream vision tasks. While deep learning has revolutionized image deblurring, most methods focus on processed sRGB images 22
46
49
13
22 6) which suffer
from irreversible information loss during the image signal processing (ISP) pipeline processing, including dynamic range compression and nonlinear transformations M. In contrast, RAW sensor data preserves linearity and high dynamic range, offering superior restoration potential through direct processing before ISP-induced degradations [42]. Despite this advantage, RAW image deblurring remains underexplored and faces challenges in effectively handling frequency-depente blur patterns and maintaining computational efficiency.
Recent advancements in sRGB deblurring highlight the efficacy of CNNs [5] and Transformers 46 for spatial domain processing. However, directly applying these techniques to RAW data proves
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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suboptimal due to fundamental differences in noise characteristics and frequency response between the two domains (9. Furthermore, opatial-domain methods may overlook the intrinsic relationship between blur formation and frequency domain representations, where convolutional degradations appear as multiplicative perturbations [8]. While some frequency-aware architectures have emerged for sRGB restoration [26]. they often rely on computationally expensive attention mechanisms, unsuitable for RAW processing. They also lack adaptive spectral modulation capabilities that are necessary for capturing the complex frequency characteristics of RAW data.
To address these issues, we propose Frequency Enhanced Network (FrENett) a specialized RAW-to- RAW deblurring framework. Specifically. FrËNet is built upon U-Net architecture that integrates spatial and frequency domain processing to achieve efficient RAW image deblurring from three key perspectives. First, the core contribution of FrENet lies in a novel Adaptive Frequency Positional Modulation (AFPM) module, which dynamically adjusts frequency components according to their spectral positions. By leveraging a lightweight MLP to learn position-dependent modulation kernels, AFPM ensures precise control over critical frequency bands for detail recovery. Second, we incorpo- rate frequency domain skip connections to preserve high- highffrequency details that are often lost during spatial downsampling. Third, our design prioritizes computational efficiency by adopting a compact CNN-based architecture, thereby avoiding computationally expensive Transformer operations while delivering superior performance.
Experiments demonstrate that our FrENet establishes a new state-of-the-arr method for RAW image deblurring, achieving 0.69dB PSNR improvement while requiring 75% fewer MACs compared to LoFormer-L. [26] on the Deblur-RAW dataset [20]. Notably, FrENet exhibits remarkable adaptability. When applied to sRGB deblurring without any architectural modifications, outperforms specialized sRGB deblurring methods on the RealBlur dataset (29) by 0.97dB PSNR (on RealBlur-J.. These results validate our core contribution: adaptive frequency modulation enables both superior restoration quality and computational efficiency.
Our main contributions are three-fold:
We propose a dedicated RAW-to-RAW deblurring network that systematically integrates spatial and frequency domain processing, explicitly leveraging the linearity and full spectral information of RAW data through learnable frequency modulation.
A novel Adaptive Frequency Positional Modulation module is introduced to dynamically calibrate frequency components using location-dependent kernels via spectral position encoding.
An
network architecture is adopted, featuring frequency- aware skip connections for detail preservation across scales and compact convolution-Fourie hybridiza- tion to reduce computational costs.
2 Related Work
2.1 Image Deblurring in sRGB Domain
Deep learning methods have largely dominated the field of image deblurring by learning end-to-end mappings from blurred to sharp images, effectively tackling the challenging blind deblurring problem.
Early deep learning approaches utilized Convolutional Neural Networks (CNNs) 38 30 32) often alongside traditional techniques. More recent CNN architectures moved towards purely end-to-end training, employing multi-scale strategies [27
CNN-based sRGB Deblurring:
33 7 or dynamic
mechanisms [48, I2) to handle varying blur levels and scales. Networks like MPRNet [45] and HINet (4 further advanced performance through multi-stage refinement and sophisticated feature fusion. Notably, NAFNet [5] showcased the potential of a simple yet highly optimized U-shaped CNN architecture for efficient and effective deblurring by focusing on basic building blocks. Inspired by NAFNet's efficiency, our network utilizes a similar lightweight CNN backbone. However, most of these CNN-based methods primarily operate in the spatial domain and are designed and trained on sRGB data, facing a significant domain gap when applied directly to RAW data due to differences in noise, dynamic range, and processing pipeline.
Transformer- based sRGB Deblurring:
Vision Transformers (ViTs) and their variants have been adapted for sRGB deblurring [3, leveraging their capability to model range dependencies

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crucial for global blur patterns. To reduce the high computational cost of standard attention for high-resolution images, efficient Transformer designs like wisdow-based attention [2T] depth-wise
convolution-base attention [46]. and localized attention schemes 37 35
have been proposed.
Some recent Transformer-based methods, such as FFTformer [8] and Loformer 26], have explored
incorporating frequency domain analysis within their architectures, demonstrating benefits for sRGB restoration. While these methods show promise in leveraging frequency information, they are tailored for the sRGB domain, are typically based on computationally different Transformer architectures, and may not offer the fine-grained, adaptive frequency modulation capability required to address the complex frequency characteristics and noise found in RAW data.
2.2 RAW Image Restoration
Operating directly on RAW sensor data offers significant advantages for image restoration tasks. Unlike sRGB data which has undergone irreversible processing by the camera's ISP, RAW data preserves linear intensity, high dynamic range, and richer original information, providing better foundation for comprehensive restoration [
RAW Image Deblurring: While traditional methods B4 [5] explored RAW image deblurring, deep learning research specifically for R-W-to-RAW deblurring is less developed than in the sRGB domain. Liang et al. (20 pioneered this deep learning sub-area with an end-to-end framework and dataset, processing both packed multi-channel and original single-channel RAW data. MLMformer 23 later introduced an efficient Transformer for RAW restoration, including deblurring on single-channel inputs. More recently, RawIR (10) addressed realistic RAW degradation synthesis and proposed a model for joint denoising and deblurring. While these deep learning works confirm the advantages of RAW domain deblurring, they primarily operate spatially. Crucially, they often lack fine- one-grained, adaptive processing of frequency components-vitll for effectively separating blur and noise from scene details in the RAW data's 's frequency domain. Other works [1] 50 28 explore RAW data for related tasks like joint processing pipelines but do not focus on general RAW-to-RAN deblurring with explicit frequency analysis.
Other RAW Restoration Tasks: The potential of processing RAW data has been successfully demonstrated in other image restoration tasks, including denoising 14 17 44), super-resolution
39 40 19 16). and low-light enhancement 22 13 47 41) These studies collectively underscore the superior restorability offered by the RAW format. However, they address different types of
degradations (primarily noise or resolution) and largely rely on spatial domain processing techniques. Our work specifically targets the deblurring problem and explores the relatively unutilized potential of integrating adaptive frequency analysis within the RAW domain for this task.
In summary, while deep learning has achieved significant success in sRGB deblurring, including recent explorations 18 26 25] into frequency domain processing within Transformer architectures(36)
deep learning research for RAW image deblurring is less mature. Furthermore, existing RAW deblurring methods have not fully exploited the benefits of adaptive frequency domain analysis for better separation and restoration of details from blur and noise. Our proposed FrENet aims to fill this gap by presenting& a novel RAW-to-RAW deblurring framework that combines an efficient CNN backbone with a unique adaptive frequency perception module designed to leverage the specific challenges and characteristics of RAW data in the frequency domain.
3 Proposed Method
3.1 Overall Framework
Given that the frequency domain of images provides reliable information about blur patterns (24). we use the Fast Fourier Transform (FFT) for frequency-based image component analysis. Notably, image blurring, convolution in the spatial domain, corresponds to element-wise multiplication in the frequency domain, which fundamentally simplifies the restoration problem in this domain. This allows to retain high-frequency information in the original image and analyze blur patterns to guide image deblurring. Our method, Frequency Enhanced Net (FrENet), focuses on effective frequency-domain processing within the U-shape architecture, as shown in Fig. [a).
Our FrENet comprises T. scales, i.e., contains L. encoder layers, L. decoder layers, and a bottleneck layer. It starts with an initial convolutional layer that converts the input blurry image y R C.. Xx
3

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Spatial Skip Connection
Elementtwwise
FrE Block m
FrE-Block Block m93
3x3 Conv
FrE- BlcckBlckk x
Fr Block x
FrE- BlackBBcck me
3x3 Conv
Element-wise Mulliplication
FrE-Boock Block
FrE FrE- Block
Simple Gatee
FACM
FACM
Weighted Sum
Froquency Skip Connection
(a) FrENet
FFT
Is1
stretch
FFT-Shin
Froquency
Skip Connection
fij
p"p
Wij
bij
LayerNorm
reshape
Conv
Linear
Linear
1x1 Conv
3x3 Dconv
extract
GELU
GELU
Linear
Linear
SCA
AFPM
calculate distance
dy
KBG
Conv
split
Frequincy
Skip Connection
FFT-IShift
IFFT
Sprocessed
flocal
(b) FACM
(c) AFPM
The architecture of our FrENet is designed with compact convolution-Fourfe hybrid structure, ensuring efficient inference. The key module FACM integrates global frequency information via SCA and local frequency details through AFPM. Notably, AFPM incorporates learnable frequency kernels determined by their spectral positions. Cooperated with frequency skip connections, AFPM substantially enhances frequency details, leading to significant performance improvements over state-of-the-art methods.
Figure 1:
(Otn for RAW image after packing) into higher-dimensionn image features fanc E HC for subsequent processing. Each layer of the encoder, decoder, and bottleneck consists of multiple Frequency Enhanced Blocks FrrEkBlocks). The network ends with final convolutional layer that converts image features of the last decoder layer fdec e H into the final deblurred image R Cr HxW
The overall pipeline of our FrENet can be described as follows:
fonc Convax3 (V),
f.nc Encoder; (f( FrE-Block xn (fE9))iE (1,2,...,
ddec Bottleneck (f BBB-Blockxx(FFi
(1)
f Decoder; (f. FrE-Blockym, (f J _ (.eno) iE (L, - 1,..,,,1
G (onvaxa(fdoc) dde))
where n, and m, represent the number of blocks in the i-th scale of the encoder layer and decoder layer, / denotes the number of blocks in the bottleneck of the architecture. In the encoder, as the scale increases, the spatial resolution of the feature halves, and the channel number doubles, i.e., frne E 2'xx # W. In the decoder, this process is symmetric, with the spatial resolution of the
feature doubling and channel number halving, i.c., Sdec 12 Cx & Our key innovations

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1) Both frequency-domme domann and spatial -omiiin skip connections are set up between each encoder and decoder layer feed the spatial feature f! and frequency feature f! to the decoder layer. The frequency skip connection feature f!ene in the i-th scale is sourced from the n:-th FrF--Block. This enables richer transmission of multi-scale spectral information along the encoder-decoder path. 2) Designing FrE-Blocks as the core processing units within each U-Net layer to directly handle frequency features, where both global and local frequency information can be well exploited for improving deblurring performance. We describe the details of the core unit FrE-Block in the following.
3.2 Frequency Enhanced Block
Each FrE-Block consists of Frequency Adaptive Context Module (FACM) and Feed-Forward Network (FFN) as shown in Fig. [). For the structure of FFN, we adopt an implementation consistent
with that used in Restormer 46. The details of the FFN structure are provided in Appendix A
The details of FACM are shown in Fig. [b). We take the first scale as an example. For simplicity, the input and output of each block are denoted as fin and fout. respectively, without using subscripts. Given the feature spatial domain fin E RC'Hixx we first adopt the FFT operation to convert the image from the spatial domain to the frequency domain and use the shift function FFT-Shift to recenter the rero-frequency component of the spectrum to the middle. If the FrE-Block in a decoder layer, we adopt frequency conneeoonectinn. The initial FFT-transformed feature is summed with the frequency domain feature from the corresponding encoder layer's last FrE-Block (which is the frequency domain output before IFFT), and the result is used as f free. If the FrE- FrE-Block is in an encoder layer or the bottleneck layer, the FFT-transformed features are directly used as f freg- Considering that the values of features become complex numbers after the FFT, concatenate their real and imaginary parts along the channel dimension. To mitigate the significant data distribution changes inherent in FFT, layer normalization (LN) is applied to stabilize the frequency features:
foorm LN(Concat(R(f freg)))
where R, J represents the real and imaginary parts, respectively.
Then, the frequency features fnorm undergo initial shared processing designed to extract foundational frequency patterns. A between real and imaginary components. Next, a 3 x 3 depthwise convolution is applied to calculate local correlations between frequency bands in the frequency domain. SimpleGate (SG) activation function [5] is then applied to obtain intermediate feature f processed:
convolution is first applied for channel-wise information fusion, such as
fprocessed = (DCooVVxx3CConv/ (fnorm)))
The resulting feature map Spracessed is then processed through two parallel branches to capture both local details and global context within the frequency domain: 1) Local Frequency Feature Enhancement Branch and 2) Global Frequency Context Branch.
This branch feeds fprocessed into our proposed AFPM module. Due to the fact that different positions in the frequency domain represent image signals of different frequency bands, the proposed AFPM adaptively modulates features by generating position-aware weights. It adjusts features across different frequency bands based on their locations in the frequency domain, unlike traditional approaches that often apply uniform operations in learnable operations that
Local Frequency Feature Enhancement Branch:
different positions. To achieve this, AFPM employs position-sensitioo enhance the representation of specific frequency bands. We first divide the frequency feature map Spracessed E HC into multiple feature patches, each sized C x xpp. X p:
fu
fin
H
W
f Sprceessed
m
n1
P
P
fm1
mm
We use the center of each patch as its position index ij to indicate its relative location in the original feature map. The distance from the patch to the center of the feature map is defined di as shown in Fig. Mc). We apply two Kernel-Bias Generators (KBGs) to dynamically generate two position- specific components, i.e., position-awan aware kernels and biases conditioned on distance dy E B 1xx1x1
5

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KBG contains two fully connected layers and GELU activations in between, By feeding the distance d into two KBGs, we can obtain position aware kernels wij E R 1xxxp and biases big E R respectively. The kernels and biases adaptively adjust the significance of local frequency features for the current restoration task. Each kernel and bias is shared across channel dimension. Subsequently. following this kernel-bias application, we apply 1 X 1 convolution across the channel dimension to further refine and adjust the channel features. Taking the patch with index ij as an example, we use a weighted sum operation to combine the kernel with the frequency-domal patch and add the bias to adaptively adjust the frequency-domain features. This process is expressed as follows:
ff)) (Convix1(ws) fiz + b,y)) fi)
(5)
where represents the weighted sum operation, C represents the element- wise multiplication. Notably, we can compute all patches in parallel and place them back according to their index to obtain flocal-
More information of AFPM is provided in Appendix B
Global Frequency Context Branch: Operating in parallel, this branch takes the same features as
input, and applies Simplified Channel Attention (SCA) mechanism from NAFNet (5) It aggregates global spatial information across the frequency map and computes channel-wist attention weights, allowing the network recalibrate features based on global frequency context. It can be represented as:
globbll (CC[[foocessee (Convix1 (ArgPool
@ processed
(6)
where AvgPool represents the average pooling.
The features from the local branch flocal and the global branch falobat are then fused, typically via element-wise addition:
f Siocat + Salohat
This fused feature map ffused is passed through a final 1 x 1 convolution for further feature integration and potential dimensionality adjustment. Lastly, the processed frequency-doman features are first shifted by the inverse shift function FFT-IShift to reverse the centering of the zero-frequee frequereygne component in the frequency domain, ensuring the reconstructed spatial- image is correct. If this FrE-Block is the last block of the current encoder layer, the complex-valued output at this stage is stored for the frequency domain skip connection to the corresponding decoder layer. Before applying IFFT, since the preceding operations are performed on the concatenated real and imaginary parts of the frequency-domait features, the processed feature map is first split into two equal halves along the channel dimension. These two halves are then interpreted as the real and imaginary parts, respectively, and are combined to form a complex-vale -value tensor, which is then transformed back to the spatial domain using IFFT, yielding the output feature map fout-
4 Experiments
4.1 Experiment Setup
We evaluated two model configurations based on the FrENet architecture: FrENet was configured with a feature width of 32 and 24 processing FrE-Blocks, and FrENet+ employed feature width of 64 and 20 FrE- Block.. Within every AFPM, we divide the feature map into an 8 x 8 grid of non-overlapping patches. If downsampling leads to very small feature map sizes (< 8 X 8), adopt a coarser granularity in this layer.
Model Configuration:
Dataset: We evaluate our method on five datasets: Deblur- (20 the RAW domain, and GoPro 27 HIDE (II, RealBlur-R, R, and RealBlur-J (29] in the sRGB domain. For the HIDE dataset, we specifically evaluate our method using the model trained on the GoPro dataset.
Implementation Details: For the Deblur-RAW 200 dataset, we adopted the preprocessing method- ology used by RawNet [20]. This involved subtracting the black level and subsequently normalizing
the raw data to the range [0, 1] by dividing by the maximum signal value. During training. 128 x 128 patches were randomly cropped from the normalized single-channel raw images. These single- channel patches, containing the RGGB Bayer pattern, were then packed into a 4-channel format which served as the network input. We employed the Adam optimizer with a batch size of 16. The initial learning rate was set to 0.001 and decayed using cosine annealing scheduler over 1000 training epochs. All models evaluated on the Deblur- RAW dataset were trained by us on NYIDIA RTX 5880 Ada Generation GPU.

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Blur
Sharp
NAFNet64
FFTFommr
LoForme-LL
FrENet(Ours)
FrENet+((Ors)
Figure 2: Visual results of RAW blurry images. The images are visualized after being processed into
sRGB domain using the LibRaw pipeline.
For extension to sRGB images, we only changed Ctn = 3 in FrENet without any other modifications. For the RealBlur [29] dataset, we used the same settings Deblur-RAW except that training was conducted using 256 x 256 x patches. For the GoPro dataset (27, we adopted training configurations
from NAFNet [5].
For evaluation on test sets, we employed the sliding window strategy g to process full-resolution images. The sliding window size equal to the training patch size, and the overlap size was half of the window size. Specifically, for the RealBlur test set, we utilized the official image alignment method provided by the dataset creators (2)), ensuring fair comparison.
Loss Function: In terms of the loss function, we used a weighted sum of &1 loss and Frequency & - 0.01C where Lúr = ||=(î) - F(I)ll, and 1, I,
Reconstruction (FR) loss Liri represent the deblurred image, the ground-truth and FFT operator, respectively.
Evaluation Metrie: We evaluate the performance of image restoration methods using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) primary image quality metrics. Model efficiency, including Multiply-Accumen parameters (Params), is calculated using relevant Python libraries.
Operations (MACs) and the number of
4.2
Main Results
4.2.1
Evaluation on RAW Image Deblurring
Table I: Comparison of different image restoration methods the Deblur-RAW dataset. PSNR and SSIM values are average scores evaluated in the RAW domain. MACs and Params are calculated on 128 x 128 x patches.
Methods
PSNR
SSIM
MACs(G)
Params(M) 4
NAFNet64
40.35
0.982
3.96
67.79
DeepRFT
24
42.40
0.988
5.05
9.55
Restormer
46
42.86
0.989
8.82
26.10
Stripformer
35
42.97
0.991
10.62
19.71
FFTFormer
18
43.96
0.991
8.22
14.88
LoFormer-S
26
42.97
0.990
3.27
16.36
LoFormer-L
226
44.04
0.992
8.98
48.98
FrENet(OUrs)
44.73
0.993
2.22
19.76
FrENet+(Ours)
45.63
0.994
7.30
48.38
As shown in Table 1 our proposed methods, FrENet and FrENet+, demonstrate superior performance on the Deblur-RAW dataset. Our FrENet+ model achieves state-of-the-art image restoration quality with the highest PSNR (45.63 dB) and SSIM (0.994). Our FrENet model also delivers strong perfor-

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mance (PSNR 44.73 dB, SSIM 0.993), surpassing prior methods like LoFormer-L and FFTFormer, while being remarkably efficient. FrENet achieves the lowest MACs (2.22 G) among all compared methods, presenting an excellent balance of quality and computation. FrENet has 19.76M parameters; FrENet+ offers higher quality with 48.38M parameters and 7.30G MACs.
4.2.2 The Advantage of RAW Image Deblurring
Table 2: Effectiveness of Deblurring in RAW vs. sRGB Domain. We compare two pipelines: (i) deblurring in the RAW domain before ISP conversion, and (ii) deblurring the sRGB domain after ISP conversion. Both pipelines use the identical LibRaw ISP. PSNR/SSIM are evaluated in the final sRGB domain.
Methods
Pipeline
PSNR
SSIM
LoFormer-L.
226
(i)
35.66
0.9656
LoFormer-L 26
(
31.26
0,9565
FrENet
(i)
36.12
0.9683
FrENet
(ii)
31.39
0.9574
We compared RAW vs. sRGB deblurring. To ensure a fair comparison, we used an EXIF tool to copy the original metadata to the deblurred RAW and utilized an identical LibRaw ISP for both pipelines. Table confirms the pre-ISP approach is superior, with FrENet gaining 4.73 dB PSNR. This shows that the ISP's lossy operations irretrievably degrade information, making Pre-ISP restoration essential.
4.2.3 Evaluation on sRGB Image Deblurring
Table 3: Quantitative comparison on sRGB datasets. indicates that the results are inferred using the checkpoint provided by the author, which are not reported in 226
Synthetic
Real-world
GoPro
HIDE
RealBlur-R
RealBlur-J
Methods
PSNR f
SSIM f
PSNR f
SSIM 1
PSNR f
SSIM f
PSNR t
SSIM f
Restormer
32,92
0.961
31.22
0.942
36.19
0,957
28,96
0.879
DeepRFT+
24
33.52
0.965
31.66
0.946
40.01
0.973
32.63
0,933
FFTFormer
18
34.21
0.969
31.62
0.945
40.11
0,973
32.62
0.932
LoFormer-B
26
33.99
0.968
31.71
0.948
40.23
0.974
32.90
0.933
LoFormer-L.
26
34.09
0.969
31.86
0.949
40.60*
0.976'
32.88*
0.936'
FOurst+(Ours)
34.11
0.969
31.92
0.949
40.74
0.975
33,87
0.939
Table 3 provides a quantitative comparison of FrENet+ on synthetic (GoPro, HIDE) and real-world (RealBlur-R, RealBlur-J) sRGB deblurring datasets. FrENet+ demonstrates strong performance across all benchmarks. On synthetic datasets, it achieves leading results on HIDE (PSNR 31.92 dB, SSIM 0.949) and competitive performance on GoPro (PSNR 34.11 dB, SSIM 0.969). Notably, our method excels on the challenging real-world datasets. FrENet+ sets a new state-of-the-art on RealBlur-R with the highest PSNR (40.74 dB) and SSIM (0.975), significantly outperforming prior methods. Similarly, on RealBlur-J, it achieves the highest PSNR (33.87 dB) and SSIM (0.939), showing clear advantage. These results highlight FrENet+'s effectiveness and robustness for diverse sRGB image restoration tasks, particularly in real-world scenarios.
4.3 Ablation Study
We conduct comprehensive ablation study to investigate the contribution of different components and design choices within our proposed FrENet, evaluated on the Deblur-RAY RRW dataset. The results presented in Table and Table 6 Our study focuses on: (1) the necessity of frequency domain modeling, (2) the roles of Spatial and Frequency Skip Connections FrENet, and (3) the effectiveness and internal mechanisms of FrB-Block.
Necessity of Frequency Domain Processing: To validate our core design choice operating primarily in the frequency domain, created spatial-only analogue, FrENetSpatial. This was

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Blur
Sharp
Restormer
FFTForeer
LoFormer-I.
(rFrNet+(OOrr))
Figure 3: Visual results of real blurry images from RealBlur-J dataset.
Table 4: Ablation on the necessity of frequency domain processing.
Method
PSNR
SSIM
FrENetSpatial FrENet (Ours)
41.89
0.9894
44.73
0.9931
achieved by removing all FFT and iFFT operations from our architecture, forcing all modules like convolutions process features purely in the spatial domain. As shown in Table the performance collapses: PrENetSpatial achieves only 41.89 dB PSNR, while our full FrENet reaches 44.73 dB. This massive 2.84 dB gain confirms that processing features within the frequency domain is the primary driver of our model's success.
Table 5: Ablation study of spatial and frequency skip connections.
Spatial Skip Connection
Frequency Skip Connection
PSNR
SSIM
X
43.91
0.9899
44.39
0.9927
44.73
0.9931
Effectiveness of Frequency Domain Skip Connections: We evaluate the contribution of the skip connections within the overall FrENet's UNet architecture. As shown in Table 5 the full architecture, incorporating both Spatial and Frequency skip connections, achieves 44.73 dB PSNR and 0.9931 SSIM. Ablating the proposed Frequency Skip Connection while retaining the standard Spatial Skip Connection leads to a noticeable performance decrease to 44.39 dB PSNR and 0.9927 SSIM. This demonstrates the significant positive impact of integrating Frequency Skip Connection, complementing the spatial information from the traditional Spatial Skip Connection and highlighting key architectural innovation of FrENet.
cequuency-domalie information via the
Table 6: Ablation study FrE-Block.
Method
Local Branch
Global Branch Division Granularity
PSNR
SSIM
Average Pooling
x 8
44.35
0.9927
2 2
44.44
0.9928
X 4
44.52
0.9929
Adaptive
x 8
44.48
0.9928
8
44.67
0.9930
8 8
44.73
0.9931
Effectiveness of Local and Global Branches: As shown in Table 6 we evaluate the interplay between our Local Branch and Global Branch under otherwise identical settings. Our full proposed model, combining both branches, achieves the best performance (44.73 dB PSNR, 0.9931 SSIM). Ablating the Global Branch and using only our Local Branch leads to slight performance de- crease (44.67 dB PSNR, 0.9930 SSIM), indicating the Global Branch's complementary contribution. Conversely, ablating Local Branch and relying solely the Global Branch results in a more

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significant performance drop (44.48 dB PSNR, 0.9928 SSIM), highlighting the critical importance of our AFPM.
Effectiveness of Division Granularity: To investigate the influence of the division granularity within the FrE-Block's feature processing, compared different division sizes while keeping both the Local and Global Branches active. As shown in Table decreasing the granularity from 8 x 8 to 4 x and further
2 consistently leads a performance drop. Specifically, changing from the optimal 8 x 8 division (64 patches) results in a decrease from 44.73 dB to 44.52 dB PSNR and 0.9931 0.9929 SSIM for the 4 division (16 patches), and even lower scores (44.44 dB PSNR, 0.9928 SSIM) for the 2 x division (4 patches). This trend clearly indicates that finer- ener-grained division is more effective for extracting and modulating features, likely enabling the module capture richer and more detailed local contextual and positional information within the feature map.
Effectiveness of Adaptive Modulation: As shown in Table 6 compare our proposed adaptive modulation mechanism within the AFPM to a variant that employs a simpler, fixed pooling strategy for feature aggregation. The results indicate that the performance of the pooling-based approach is significantly inferior to that of our proposed adaptive modulation mechanism. This comparison not only validates the effectiveness of our AFPM but also highlights its superiority in adaptively processing frequency-domat features compared to the fixed pooling-based method.
Limitations
Despite achieving state-of-the-an deblurring performance and being more efficient than Transformer- based methods, FrENet has limitations. First, the heavy reliance on FFT and IFFT introduces substantial computational cost for high-resolution images. Second, the AFPM's simplified positional encoding may fail to fully capture complex spatial dependencies, indicating the need for more advanced techniques.
6
Conclusion
Based on the importance of analyzing image frequency characteristics for image restoration, we propose the Frequency-Enhance U-Net (FrENet). FrENet integrates traditional spatial domain skip connections with frequency domain skip connections and operates directly frequency features within its core processing unit, the FrE-Block. The FrE-Block contains Frequency Adaptive Context Module that utilizes Adaptive Frequency Positional Modulation for local frequency details and Simplified Channel Attention for global spectral context, enabling precise modulation and utilization of frequency features significantly improve image restoration performance. Furthermore, the adaptive frequency modulation mechanism demonstrates potential applicability to other image restoration tasks in RAW domain.
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (62576241, 62172127, U23B2049 and U22B2035), the Natural Science Foundation of Heilongjiang Province (YQ2022F004).
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Answer: [NA]
Justification:
The paper does not involve LLMs as any important, original, or non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy (https://neurd) hbtre://eeerrpppcc/o//ofeeeeee// /c/////oeeeeeeeee/222////.. for what should or should not be described.
20

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Appendices
A Details of FEN
Our FrE-Block utilizes a FFN module adapted from the Gated-Dcony FFN structure introduced in Restormer [46]. Given an input feature fin E xC the FFN processes through parallel branches followed by an element- wise product and residual connection.
The first branch processes fin through 1 1 convolution for channel expansion, x 3 depth-wiss. convolution for spatial feature mixing within each channel group, and a GELU activation function. The second branch processes fir through 1 x 1 convolution followed by 3 x 3 depth-wise convolution.
The outputs of these two branches are multiplied element-wise, acting content-aware gating mechanism that selectively modulates the features. Finally, a convolution projects the features back to the original channel dimension, and residual connection is added to the input fin.
The structure can be mathematically represented as:
fout f... + Conv x (BLLUDDCarVsxx(Coo/ii (fi (DConvsxs(Convixl (f (I)
where Convix1 denotes 1 x convolution (often with channel expansion/reduuctio internally). DConVax3 3 3 depth-wisse convolution, GELU the Gaussian Error Linear Unit activation, and @ represents element-wise multiplication. This structure allows the network to learn complex feature transformations while maintaining computational efficiency and capturing local spatial context.
21

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B Discussion of AFPM
This appendix provides a detailed discussion of the AFPM module, the core component of our FrE-Block designed for position-aware frequency feature refinement.
As discussed in the main text, different locations in the frequency domain represent image content at different spatial frequencies (low frequencies near the center, high frequencies towards the periphery). AFPM leverages this property to adaptively process features based on their spectral position.
The module takes a frequency feature map processed E Cx W as input. We first divide fprocessed into a grid of non-overlapping patches fúj E IRxxpx, where (i.j) indicates the patch's 's row and column index in the grid, and the grid has m = H/p rows and n W/p columns.
For each patch (i.j), we first compute its normalized Euclidean distance dij E R from the patch's ss center to the center of the entire feature map. This scalar distance du serves as proxy for the dominant frequency range covered by this patch and is used as input to the Kernel-Bin Biias Generators.
AFPM then employs two lightweight Kernel-Biae Generators (KBGs). Each KBG is an MLP consisting of two linear layers with an intermediate GELU activation, implementing non-linear mapping. These KBGs dynamically generate position-aware parameters conditioned on d.y:
A kernel Way 1xpxP: Generated by KBC specific to the patch's 's frequency location and is shared across all C' channels.
(d.y). This kernel provides spatial weights
A bias bij E RIxxxl; Generated by KBGbi (d.z). This provides a location-specific bias, also shared across channels.
These generated parameters used to modulate the patch features. The modulation is performed as follows for each patch fly:
AFPM(f()) = (Convix (wiy Sij + biy)) o Sij
Here, Wiy+ fiz denotes a channel-wise weighted summation: for each channel, the x feature slice of fig is element-wise multiplied by the shared 1 x p x p kernel wiy. and the results are then summed spatially, yielding an intermediate feature of size xxIIx1 The position-awee -wware bias b,j (broadcast to gCx1xl) is added to this aggregated feature. Subsequently, a 1 convolution processes this RCx1x1 tensor. This convolution facilitates channel-wise interactions and transforms the aggregated, position-biased information into refined per-channel modulation factor of size IR Cx1x1, Finally, this modulation factor is broadcast back to the patch dimensions IR Cxxxx and element-wise multiplied (O) with the original patch features fiy. This process allows AFPM to learn channel- wise scaling factors that adaptively enhance or suppress frequency components within each patch based on its location in the spectrum.
The adaptive, position-aware modulation performed by AFPM is visually demonstrated in Figure B. (which includes feature maps and generated kernels way in the RAW domain) and Figure B.2 (feature maps in the sRGB domain). The KBGs' behavior, central to AFPM's adaptivity, is particularly evident from the visualized kernels w Figure B.I These 1 X p X p kernels are shown for different spectral patch locations across various network stages (Encoder,, Bottleneck, Decoder,) ). In these kernel visualizations, darker colors indicate higher learned weight values, and lighter colors represent lower values. The indices i, in Wi.j (e.g., Wo.0. W1.1, W2,2, W3.3 shown in the figure) correspond patches at varying normalized Euclidean distances dyy from the center of the frequency map. For instance, Wooo represents the kernel for a patch in peripheral, top-left if following standard image indexing), while kernels with higher indices like W3.3 (assuming a 4 x 4 display of kernels) are progressively closer to the center, corresponding to lower-frequency regions.
figh-frequency region (e.g..
Observing the visualized kernels wij and feature maps in Figure B.I and Figure B.2 reveals several key aspects:
The strueture and intensity patterns of the kernels waj in Figure B.T I change with their spectral position. For exam- ple, in Encoder; of Figure B.I the peripheral kernels (e.g., wp,0, w1,1) appear to have more distinct, higher-intensity (darker) patterns compared to the more central kernels (e.g..
Positional Specificity of Kernels and Modulation:
22

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W2.2, Ws.s), which might be smoother or have generally lower intensity values. This sug- gests that AFPM learns to apply stronger or more structured modulation to -requunyy components (potentially to enhance details or suppress specific noise patterns) and differ- ent, perhaps more subtle or uniform, modulation to low- now-frequency components (to adjust overall brightness or contrast in those bands). The resulting AFPM( processeed) maps reflect these position-speciffe modulations. This directly confirms that the KBGs learn to generate distinct spatial weighting strategies based on the input distance dij.
Layer-wise Adaptation and Effect on Features: The characteristics of the learned kernels and their impact on feature maps adapt across different network layers and domains:
In early encoder stages (e.g., Encoder, in both Figure B. and B.2 AFPM appears to perform initial spectral refinement. The kernels might be learning to selectively boost certain structural frequencies or perform gentle equalization. The (FPM((frrocssse) maps show subtle but widespread adjustments compared to Sprocessed-
In the bottleneck (middle row of both figures), features are highly compressed. The kernels w (in Figure B. I) appear to adapt to this abstract representation, and the processed)
resulting fproeseraeessed shows more pronounced, localized changes, likely focusing on preserving or transforming features critical for the decoder.
In decoder stages (e.g.. Decoder,, bottom row of both figures), the modulation is critical for reconstruction and often more aggressive. In Figure B (RAW), the kernels wi. for Decoder, might learn to strongly amplify frequencies corresponding to edges while suppressing others. This is reflected in AFPM ffoMespoocssed where structured details appear sharpened. Notably, in Figure B.2 (sRGB) for Decoder,, specific patterns in processed that resemble blur artifacts (e.g.. diffuse diagonal bands or "ghosting") are visibly suppressed or transformed in AFPM AFPM( (processed) leading to cleaner fout- This targeted suppression the sRGB domain highlights AFPM's ability to adapt its frequency modulation to combat different manifestations of blur.
Content-Independdet Nature of Kernels: It is crucial to reiterate that the kernels w.j themselves are generated based only on the patch's 's spectral position dij. not its content fuj- The visualization of Wej in Figure B. thus purely reflects the learned spatial modulation strategy for a given frequency location. The actual content adaptation occurs when this position-specific kernel modulates the patch features fiz as per Equation 2
This layer-dependent and position-specific modulation capability, evidenced by both the overall feature map transformations (Figure [B.1] Figure (B.2) and the varying structures of the generated kernels (Figure [B.I), arises directly from the KBGs. By allowing the network to learn how to weight and shift frequency components based on where they are in the spectrum, AFPM provides powerful and flexible mechanism for adaptive frequency domain processing.
In the main paper, our ablation studies on division granularity (comparing 2 x 2, 4 4, and 8 x 8 grids for patch division within AFPM) demonstrated that a finer- grainerrindd division generally leads to better performance, with the 8 x grid yielding the best results among those tested. This suggests that enabling AFPM to operate on more localized spectral regions allows for more precise modulation. For instance, we also experimented with fixing the kernel size of way to 4 x 4 while increasing the number of patches (i.e., making the grid finer than 8 x 8 such that each patch is smaller than 4 x 4 is possible, rather, if the feature map is H x W, a le x k grid means p H/k, W/k. R.. If is fixed at 4, then a larger H, W means more patches). Conceptually, if each patch becomes very small (e.g.. if p itself was reduced, like using p = 4 for the patch size which Way operates on, and having more such patches in a larger grid), this could offer even more precise control, and preliminary tests indeed showed further improvements. However, this significantly increases the number of KBG evaluations (if each patch gets its own kernel) or the complexity of managing these smaller patches, leading to higher parameter counts and computational costs. Therefore, the 8 x 8 grid represented a good balance of performance and efficiency for our reported results.
23

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Maa
WILL
First AFPM in Encoder,
Fareersndd (64 x x.6
NFM precemse X64 64)
Fost (64
First AFPM in Bottleneck
Sroccoee R)
AF procemes 1 B)
fos (9 x B)
W1..
W4.4
Last AFPM in Decoder,
W4.3
W.:
[scaceree AFPM)
Figure B.1: Multi-level feature visualization of the key stages around AFPM in the RAW do- main. Each row shows the input frequency feature map (processed ), the output after AFPM (AFPM(J prrocesse)))) the spatial domain output of the block (fout) and kernels wi, generated by KBGs from a representative block in Encoder,, Bottleneck, and Decoder, Deeper colors represent higher values.
FirstAFPM in Encoder,
processod 256)
proceseed 256)
car
in in Bottleneck
prosessed (32
AFPM proceued ]32 x2)
(32
in
Frrncessoa (256 x 256)
(F (256 256)
faur (256 x 256)
Figure B.2: Multi-level feature visualization of the key stages around AFPM in sRGB domain. Each row shows the input frequency feature map [processeed ), the output after AFPM (AFPM(J (frroe(ppreessed)) and the spatial domain output of the block (fout) from representative block in Encoder1, Bottleneck, and Decoder,. Deeper colors represent higher values.

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C Performance and Efficiency Analysis
C.1 Inference Efficiency on Full-Resolution Images
While the main body of the paper analyzes computational efficiency on the 128x 128 patches used during training, crucial evaluate the model's practical performance on full-resolution images, which is the typical use case. To this end, we employ a standard sliding-window approach for inference. The high-resolution input image partitioned into overlapping 128x128 patches, processed independently, and the results are then stitched together to reconstruct the final output. This strategy common practice the field, also adopted by competing methods like LoFormer[26) and DeepRFT (24), ensuring a fair comparison.
We benchmarked the end-to-end inference time and GPU memory usage on full-resolution RAW images using single NVIDIA RTX 5880 Ada GPU. As shown in Table CCT]] the results confirm that our model's efficiency on small patches translates significant real-world advantage. FrENet is demonstrably faster (1.36x to x and more memory-efficient than powerful Transformer-basee baselines in this practical, end-to-end scenario.
Table C.I: Efficiency Comparison Full-Resolution RAW Images.
Methods
Params(M)
GPU Memory(MB)) Runtime(ms)
Restormer
46
26. 10
1238.71
102.95
FFTFormer
18
14.88
2193.03
222.56
LoFormer-L
26
48.98
2391.10
222.49
(OuNe)+(Ours)
19.76
1083.30
75.36
C.2 Modulc-Level Efficiency Analysis
To provide deeper understanding of the computational cost distribution within our model, we present a module-level analysis a single FrE-Block. The analysis, detailed in Table C.2 reveals that our proposed core components, the AFPM and SCA modules, are highly parameter-efficient. Combined, they account only 28. of the model's total parameters and mere 1.8% of the MACs.
The majority of parameters and computational load are attributed to standard architectural components, such as the Feed-Forward Network (FFN) and convolutional layers. This breakdown underscores that our model's performance gains stem from the targeted and efficient design of its novel modules rather than an increase overall model complexity.
Table C.2: Per-Module Cost Analysis of FrENet on 128 128 size patches.
Module
Params(M)
MACs(G)
Runtime (ms)
Convolutional Layers
6.0 (30.4%)
0.92 (41,4%)
0,149 (16.0%)
AFPM Module
2.86 (14.4%)
0.03 (1.4%)
0.103 (11.1%)
SCA Module
2.71 (13.7%)
0.01 (0.4%)
0.039 (4.2%)
Others (e.g.. Layernor)
8.29 (41.9%)
1.26 (56.7%)
0.639 (68.7%)
Total
19.76
2.22
0.93
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C.3 Visualization of Comparison
Figure presents quantitative comparison various image deblurring methods on the Deblur- RAW dataset, illustrating the trade-off between deblurring performance (PSNR, Y-axis) and compu- tational efficiency (MACs, X-axis). The size of each bubble indicates the model's parameter count. Positioned in the upper-left region, our proposed method FrENee demonstrates superior performance significantly lower computational cost. Specifically, FrENet achieves state-of-the-e deblurring performance. Compared to competitive methods like LoFormer-L, which achieve comparable PSNR, FrENet requires considerably fewer MACs. This plot clearly showcases FrENet's effectiveness in balancing high restoration quality and computational efficiency on the Deblur-Re RAW dataset.
46.0
45.5
FrENet(OOus)
LoFormer-L
44.5
FFTFormer
44.0
43.5
LoFormer-S
Restormer
PSNR (dB)
DeepRIT
42.5
42.0
NAFNet64
40.5
40.0
39.0
MACss (G)
Figure C.3: Performance (PSNR) and efficiency (Params, MACs) comparison of various image deblurring methods on the Deblur-RAW dataset.
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D
Visual Results
Blur
Sharp
NAFNet64
FFTFormer
LoFormer-L
FrEnNe(OUuss)
FrENet+(Ours)
Blur
Sharp
NAFNet64
FFTFormer
LoFormer-L
FrENet(Ours)
(Ours)
Figure D.4: Visual results on the Deblur-RAW dataset.
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Blur
Sharp
NAFNet64
FFTFormer
LoFormer-L.
FrENet(Ours)
FrENet+(Ours)
MOLT and
MAS
Blur
Sharp
WODE
MOLL MASI
MOLL
MASI
MASI
NAFNet64
FFTFormer
LoForre-LL.
MOLK and MASI
MOL and MASI
FrENet(OUrr)
(Ours)
Figure D.5: Visual results on the Deblur-RAW dataset.
28

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Blur
Sharp
Restormer
FFTForme
LoFormer-L.
FrENect+(Uus))
Figure D.6: Visual results the RealBlur-J dataset.
29

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DI DU
DRE
T
S16
616
616
616
616 848
616
04S
KOM
ROM
Blur
Sharp
Restormer
FFTFormer
LoFormer-L
FrENet (Ours))
Figure D.7: Visual results on the RealBlur-R R dataset.
30

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Blur
Sharp
Restormer
FFTFomer
LoF FormerLLL
FrEnct-(Ours)
Blur
Sharp
Restormer
FFTFormer
LoForee-LL
FrENett((Ous))
Blur
Sharp
Restormer
FFTFormer
LoForme-LL
(Ouus)))(Ous))
Figure D.S: Visual results on the GoPro dataset.
31

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Blur
Sharp
Restormer
FFTFormer
LoForme-LL
FrEnet+(OUrs)
Blur
Sharp
Restormer
FFTFormer
LoFormer-L
FrENet+((Ors))
Blur
Sharp
Restormer
FFTFormer
LoFormer-L.
FrEnet+((Urs)
Figure D.9: Visual results the HIDE dataset.
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E
Boder Impacts
While advanced deblurring algorithms offer simple image enhancement to the public, their use also raises concerns about potential malicious applications, especially regarding privacy issues, Blurring often used to protect personal information, such as faces and personal IDs. To prevent potential misuse, image forensics algorithms can be used, which are designed to authenticate images. Many of these algorithms focus on training classifiers to distinguish between images captured the real world and images processed by deep learning models.
33


Reviews:
[{"review_id": "Ynb0tMs0E5", "reviewer": "Reviewer_Beyz", "summary": "The 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.", "questions": "Kindly 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.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thank you for the detailed rebuttal. It does address all of my questions and almost all of my concerns. Having checked the other reviewers' comments and author's responses, I am now confident that the paper has sufficient merit. I am raising my score to Accept.", "rebuttal": "We sincerely thank the reviewer for the detailed and constructive feedback. Your insightful questions have helped us improve the clarity and completeness of our work. We address each of your points below and will incorporate these clarifications into the final manuscript.\n\n---\n### **Response to Weakness 1 & Question 2: AFPM's Positional Encoding**\n\nWe appreciate the reviewer's excellent point regarding the directional nature of motion blur. This is a crucial aspect we considered during our model's development. Our final design, which uses only Euclidean distance as the positional encoding, was chosen after exploring more complex, direction-aware alternatives, including the **2D sinusoidal coordinates** suggested by the reviewer.\n\nSpecifically, we conducted experiments to compare our simple distance-based encoding with two direction-aware alternatives: one using **Polar coordinates (radius $\\gamma$, angle $\\theta$)** and another using **2D sinusoidal coordinates**. The results on the Deblur-RAW dataset are as follows:\n\n**Table: Comparison of Positional Encodings in AFPM on Deblur-RAW.**\n| Positional Encoding | PSNR (dB) | SSIM |\n| :--- | :---: | :---: |\n| Euclidean Distance $d$ (Ours) | **44.73** | **0.9931** |\n| Polar $(\\gamma, \\theta)$ | 44.68 | 0.9929 |\n| 2D Sinusoidal | 44.69 | 0.9930 |\n\nThe results are very insightful. Our experiments show that both direction-aware encodings (Polar and Sinusoidal) achieve slightly lower performance in both PSNR and SSIM compared to our simple distance-based method.\n\nThis suggests that while providing explicit directional information is a theoretically sound idea, it may introduce an overly complex learning objective for the network. The model might struggle to effectively utilize the directional signals, potentially leading to overfitting on directional artifacts present in the training data, which ultimately compromises both pixel-level accuracy and structural similarity.\n\nGiven that our simpler, distance-based approach consistently yields superior results across both primary metrics, we concluded that it offers the most robust and effective solution. It guides the model to focus on inverting the primary low-pass filtering effect of blur—the most critical aspect for restoration—which proves to be a more direct and successful strategy. This empirical finding is why we adopted the distance-only approach in our final model.\n\n---\n### **Response to Weakness 2, 4 & Question 1: Inference Strategy, Efficiency, and Hardware Information**\n\nWe would like to clarify our inference strategy and provide the requested practical efficiency metrics. We follow the standard practice of using a sliding-window approach, which is also adopted by competing methods like LoFormer [26] and DeepRFT [24], to ensure a fair comparison on high-resolution test images.\n\nA key concern with this approach is often the runtime overhead. However, we want to emphasize that our model's highly efficient architecture translates to a significant real-world performance advantage. We have benchmarked the end-to-end inference time and GPU memory usage on full-resolution RAW images on a single NVIDIA RTX 5880 Ada GPU.\n\n**Table: Efficiency Comparison on Full-Resolution RAW Images.**\n| Method | Params (M) | GPU Memory (MB) | Runtime (ms) |\n| :--- | :---: | :---: | :---: |\n| Restormer [46] | 26.10 | 1238.71 | 102.95 |\n| FFTFormer [18] | **14.88** | 2193.03 | 222.56 |\n| LoFormer-L [26] | 48.98 | 2391.10 | 222.49 |\n| **FrENet (Ours)** | 19.76 | **1083.30** | **75.36** |\n\nThe results clearly show that FrENet is significantly faster (1.36$\\times$ to 3$\\times$) and more memory-efficient than these powerful Transformer-based baselines in a practical, end-to-end scenario. To clarify hardware information for trainining and testing, we will add the following sentences to **Implementation Details**: All experiments are conducted on a single NVIDIA RTX 5880 Ada GPU and our model requires approximately 20 hours of training.' \n\n---\n### **Response to Weakness 3 & Question 4: Experimental Comparisons**\n\nWe appreciate the suggestion to broaden our comparative evaluation; benchmarking against a wider range of models is indeed essential.\n\nWe have now included StripFormer [35] in our main comparison table to provide a more comprehensive view against recent efficient models. For RawIR [10] and ELMFormer [23], we were unable to include their results in our direct benchmark at this time, as their official implementations were not available for us to retrain on the Deblur-RAW dataset.\n\n**Table: Extended Comparison on Deblur-RAW. MACs and Params are calculated on $128 \\times 128 \\times 1$ patches.**\n| Method | PSNR (dB) | SSIM | MACs (G) | Params (M) |\n| :--- | :---: | :---: | :---: | :---: |\n| StripFormer [35] | 42.97 | 0.991 | 10.62 | **19.71** |\n| **FrENet (Ours)** | **44.73** | **0.993** | **2.22** | 19.76 |\n\nThe expanded comparison shows that our **FrENet continues to achieve state-of-the-art PSNR**, outperforming even recent efficient models like StripFormer by a significant margin (+1.76 dB).  As for Hybrid Attention Transformer, it is not included as its design is primarily focused on super-resolution.\n\nFinally, to reiterate for clarity, all baselines presented in Table 1 of our paper were retrained from scratch by us on the Deblur-RAW dataset. We will ensure this point is explicitly clarified in the paper.\n\n---\n### **Response to Question 3: Processing Fourier Features**\n\nThank you for this insightful question. Our approach of concatenating the real and imaginary parts of the Fourier features is a deliberate and principled design choice, grounded in both theory and empirical validation.\n\nFundamentally, the real and imaginary parts are the orthogonal components that completely define a signal in the frequency domain. Concatenating them is a direct and effective way to feed this complete information into standard, highly-optimized 2D convolutions. This method aligns with the proven techniques used in other methods like DeepRFT [24] and Fast Fourier Convolution [r1], demonstrating its robustness.\n\nTo explore alternatives, we also empirically investigated magnitude-phase concatenation. Our experiments revealed that models using this representation failed to converge during training. We attribute this instability primarily to the challenges of processing the phase component. The phase periodicity creates a highly discontinuous space that is challenging for standard loss functions and gradient-based optimizers, leading to unstable gradients.\n\nIn contrast, the real-imaginary representation is continuous and well-behaved, providing a stable learning landscape. Therefore, our findings suggest that our chosen approach offers a more robust and practical path to achieving high-performance frequency-domain processing within modern deep learning frameworks.\n\n[r1] Chi, L., Jiang, B., and Mu, Y. (2020). Fast fourier convolution. Advances in Neural Information Processing Systems, 33, 4479-4488.\n\n---\n### **Response to Question 5: Failure Cases and Limitations**\n\nThank you for this suggestion. Like most deblurring methods, our model has limitations. It can face challenges and produce suboptimal results in scenes with extremely severe and complex blur, for instance, where the motion is exceptionally large or highly non-uniform. In such cases, some residual artifacts may remain. Acknowledging this provides a more balanced view of our method's capabilities. More failure cases will be given in the revised manuscript along with disscussions. \n\n---\n### **Response to Question 6: Ablating Spatial Skip Connections**\n\nTo provide a more thorough ablation on our network's components, we have updated our results for the skip connections. The table below confirms that both spatial and frequency skip connections are vital, and their synergy is key to our model's strong performance.\n\n**Table: Ablation of Skip Connections.**\n| Spatial Skip | Frequency Skip | PSNR (dB) | SSIM |\n| :---: | :---: | :---: | :---: |\n| ✓ | ✓ | **44.73** | **0.9931** |\n| ✓ | | 44.39 | 0.9927 |\n| | ✓ | 43.91 | 0.9899 |\n\nWe hope these clarifications and new results comprehensively address the reviewer's concerns."}, {"review_id": "gNKvUUeYXS", "reviewer": "Reviewer_TpPq", "summary": "This 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.", "questions": "The 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.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\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.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your time and for providing such detailed and constructive feedback. Your insightful comments have been invaluable in helping us identify the weaknesses of our work and have provided clear directions for improvement. We appreciate that you recognized the importance of RAW image deblurring and the novelty of our proposed direction.\n\nWe have carefully considered all your concerns. Below, we address each point in detail and outline the concrete revisions and new experiments we will incorporate into the final manuscript to resolve these issues.\n\n---\n\n### **Response to Weakness 1: Lack of RAW-Specific Design Justification & Novelty**\n\nWe thank the reviewer for this critical question, which pushes us to better articulate our core contributions.\n\n**1. On RAW-Specific Design:**\n\nWe agree that a clearer justification is needed. The primary advantage of our frequency-domain approach on RAW data stems from a fundamental physical principle: **the linearity of RAW data**.\n\n*   **Physical Fidelity:** Image blur is physically a convolution operation. In the frequency domain, this corresponds exactly to an element-wise multiplication for linear signals. RAW data, being a direct representation of sensor captures, is linear. Therefore, processing RAW data in the frequency domain allows our model to learn to reverse the blur by learning a physically meaningful multiplicative modulation. In contrast, sRGB images undergo a non-linear gamma correction. Applying frequency-domain deblurring to sRGB data is merely an approximation, as the strict convolution-multiplication relationship is broken by the non-linearity. Our AFPM module is precisely designed to learn this fine-grained, position-aware multiplicative modulation, making it fundamentally better suited for the linear RAW space where the physical model holds true.\n\n*   **Noise Characteristics:** Furthermore, RAW images have simpler, more predictable noise patterns (e.g., shot and read-out noise) compared to the complex, spatially-variant, and often correlated noise in sRGB images post-ISP. The AFPM's ability to adaptively treat different frequency bands allows it to implicitly model and handle RAW-specific noise characteristics more effectively than methods designed for the complex noise distributions in sRGB.\n\n**2. On Architectural Difference from LoFormer:**\n\nWe acknowledge that we build upon a U-Net backbone, which is a common and effective practice in image restoration. However, we must clarify a key architectural distinction: **our FrENet is fundamentally a CNN-based architecture, whereas LoFormer is a Transformer-based method.**\n\nThis is a deliberate design choice motivated by the practical demands of RAW processing. RAW images from modern cameras often have very high resolutions. Transformer-based models, with their quadratic complexity with respect to spatial dimensions, face significant challenges in terms of memory and computational cost when applied to such high-resolution inputs. Our CNN-based design offers superior scalability and efficiency, making it a more practical solution for real-world RAW deblurring applications.\n\nTo better highlight our core motivation, we will revise our introduction and methodology sections to more prominently and explicitly articulate these points. This will ensure the critical link between RAW data's linearity and our frequency-domain design is unmistakable for the reader, directly addressing the need for clearer justification you have pointed out.\n\n---\n### **Response to Weakness 2: Limited Performance Gains & Efficiency**\n\n**1. On Performance Gains:**\n\nWe appreciate the reviewer's analysis and wish to contextualize our performance gains. Our model's frequency-dependent modulation is particularly effective for the complex, spatially-varying blurs found in real-world scenarios, which is proven by the significant +0.99 dB PSNR gain on the challenging RealBlur-J benchmark (33.87 vs. 32.88). We believe this demonstrates our method's practical value. The marginal gains on synthetic datasets like GoPro and HIDE can be attributed to their simpler blur synthesis, which do not fully reflect real-world degradation. The strong performance on RealBlur-J thus confirms our approach is well-suited for real-world applications.\n\n**2. On Efficiency (Parameters vs. Computational Cost):**\n\nWe agree that the parameter counts are similar. To provide a complete efficiency analysis, we also benchmarked MACs and runtime against LoFormer-L. The results below are from a head-to-head comparison on $256 \\times256$ sRGB patches.\n\n**Table: Efficiency comparison on $256 \\times256$ sRGB patches.**\n| Model| Params (M) | MACs (G) | Runtime (ms) |\n| :- | :-: | :-: | :-: |\n| LoFormer-L [26] | 48.98 | 143.67 | 75.90 |\n| FrENet+ (Ours) | 49.37 | 114.22 | 36.10 |\n\nThis comprehensive view shows that with a comparable model size, our FrENet+ requires ~20% fewer MACs and is over 2x faster than LoFormer-L. This demonstrates a more favorable balance between model size and computational efficiency, which we will highlight in the paper.\n\n---\n### **Response to Weakness 3: Unfair Experimental Comparison**\n\nThis is an excellent point, and we agree that the suggested experimental setup provides the most rigorous and fair evaluation. While our original experiments retrained all baselines on RAW data from scratch (by changing input/output channels), which provides a degree of fairness, we acknowledge the reviewer's proposed pipeline is superior.\n\nWe have conducted new experiments during this rebuttal period to address these concerns directly. We will add the following table.\n\nWe converted the Deblur-RAW dataset to sRGB using the standard LibRaw [r2] processing pipeline and re-trained the LoFormer on this new sRGB dataset. The results are tested on the sRGB images converted from the original RAW test set.\n\n**Table: Comparison on sRGB images converted from Deblur-RAW.**\n| Method |  PSNR |   SSIM |\n| :- | :-: | :-: |\n| LoFormer-L (trained on sRGB-version of Deblur-RAW)         |  31.26 | 0.9565   |\n| **FrENet (Ours, Deblurred RAW -> ISP)** | **36.12** | **0.9683** |\n\n---\n### **Response to Weakness 4: Comparing Deblurring Pipelines**\n\nWe thank the reviewer for this excellent and critical suggestion. To validate the superiority of deblurring in the RAW domain, we have conducted the requested comparison between the two key pipelines:\n-   **(i) Deblur-then-ISP:** Deblurring is performed on RAW data first, followed by LibRaw [r2] processing pipeline.\n-   **(ii) ISP-then-Deblur:** The blurred RAW image is first converted to sRGB via the LibRaw [r2] pipeline, and then deblurring is applied.\n\nWe evaluated both pipelines using two different network architectures, our **FrENet** and a strong baseline **LoFormer-L [26]**, to ensure the conclusion is generalizable. For a fair comparison, both models were trained separately for each pipeline on the corresponding data (RAW or sRGB). The results are evaluated on the sRGB images from the Deblur-RAW test set.\n\n**Table: Pipeline Comparison: (i) Deblur-then-ISP vs. (ii) ISP-then-Deblur.**\n| Network | Pipeline | PSNR | SSIM |\n| :- | :-: | :-: | :-: |\n| FrENet | (i)| **36.12** | **0.9683** |\n| FrENet | (ii)| 31.39 | 0.9574 |\n| LoFormer-L[26]| (i)  | 35.66 | 0.9656 |\n| LoFormer-L[26]| (ii)  | 31.26 | 0.9565 |\n\nThese new results will empirically validate our core claim: deblurring in the linear RAW domain before applying the ISP yields superior results due to the preservation of critical information.\n\n[r2] LibRaw: Open-source library for processing RAW files.\n\n---\n### **Response to Weakness 5: Incomplete Ablation Study**\n\nWe appreciate the suggestion for a more detailed and clearer ablation study.\n\n**1. Contribution of AFPM and SCA:**\n\nWe thank the reviewer for this suggestion. We would like to clarify that this analysis is provided in Table 4. In that table, \"Local Branch\" corresponds to using only the AFPM module, while \"Global Branch\" uses only the SCA module. The results therefore demonstrate their individual contributions and synergy. We will revise the paper to make this correspondence unambiguous.\n\n**2. Module-level Efficiency Analysis:**\n\nWe agree that a cost breakdown is essential for understanding the efficiency impact. We will add the following table to the Appendix to detail the parameter distribution of the key components within one FrE-Block. The results, presented in the table below, demonstrate that our key components, AFPM and SCA, are highly parameter-efficient. Collectively, they constitute only 28.1% of the model's total parameters. The majority of parameters reside in standard components like the Feed-Forward Network (FFN) and other foundational elements. This highlights that our performance gains are achieved through targeted and efficient architectural contributions, rather than by simply increasing the overall model size.\n\n**Table: Per-Module Cost Analysis of FrENet on $128 \\times 128$ size patches.**\n| Module | Parameters (M) | MACs (G)|Runtime (ms)| \n| :--- |:---:|:---:|:---:|\n| Convolutional Layers | 6.0M (30.4%) | 0.92G (41.4%) |0.149ms (16.0%)|\n| AFPM Module | 2.86M (14.4%) | 0.03G (1.4%)|0.103ms (11.1%)|\n| SCA Module | 2.71M (13.7%) | 0.01G (0.4%)|0.039ms (4.2%)|\n| Others (e.g., Layernorm) | 8.29M (41.9%) | 1.26G (56.7%) |0.639ms (68.7%)|\n| Total | 19.76M | 2.22G |0.93ms|\n\n---\nOnce again, we thank you for your thorough review. We are confident that by incorporating these extensive revisions, new experiments, and detailed analyses, we can address all the concerns raised and significantly strengthen the quality and impact of our paper. We believe the revised manuscript will present a more compelling case for the effectiveness and efficiency of our proposed method for RAW image deblurring."}, {"review_id": "5jQujydN7Q", "reviewer": "Reviewer_XpwB", "summary": "This 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.", "questions": "See the points in Weakness.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The contribution of this paper is significant enough for publication and I am satisfied with the response.", "rebuttal": "We sincerely thank the reviewer for their positive and encouraging evaluation. We are grateful for their recognition of our method's novelty, efficiency, and strong performance. The reviewer's insightful questions are highly valuable, and we appreciate the opportunity to clarify these aspects. We will incorporate these clarifications into our final manuscript.\n\n---\n\n### **Response to Weakness 1: The content-agnostic AFPM mechanism**\n\nWe thank the reviewer for this excellent question. Our design choice is rooted in decoupling the general blur restoration strategy from its application to specific image content.\n\n1.  **Blur is Position-Dependent in the Frequency Domain:** Fundamentally, blur acts as a low-pass filter, attenuating high-frequency components more severely than low-frequency ones. Since spectral position is a direct proxy for frequency bands (low frequencies at the center, high frequencies at the periphery), learning a restoration strategy based on position is equivalent to learning how to differentially treat various frequency bands to counteract blur. For instance, the model learns a general rule: \"components at the periphery require stronger amplification than those at the center.\"\n\n2.  **Decoupling Strategy from Content:** Our AFPM module generates modulation kernels ($w_{ij}$, $b_{ij}$) using only positional information. This kernel represents a learned, universal restoration strategy for a specific frequency location. The final modulation, however, is **content-dependent**, as this strategy is applied to the actual content features $f_{ij}$. This decoupling allows the model to learn a robust and general restoration map without being misled by specific content in any given patch.\n\n3.  **Ablation Study on Content-Dependent Kernels:** To validate this design, we performed a new experiment where the kernel generation was made purely content-dependent (by feeding $f_{ij}$ into the kernel generator). This variant yielded an inferior performance (44.59 dB PSNR vs. our 44.73 dB). This suggests that our decoupled approach, where a content-agnostic strategy is applied to content-specific features, is a more effective and robust design for the deblurring task. We will add a discussion of this insightful ablation study to the paper to further validate our design.\n---\n### **Response to Weakness 2: The use of SCA in the frequency domain**\n\nWe thank the reviewer for this insightful point and will expand on this in our revision.\n\nWhile originally for spatial data, we reinterpret SCA's operations for the frequency domain. In this context, Global Average Pooling does not average spatial pixels but computes a **\"global spectral descriptor\"** for each channel. This single value summarizes the overall energy distribution across all frequencies for that feature.\n\nThis allows the network to perform content-adaptive channel recalibration based on the image's global spectral properties. For example, the model can learn to globally suppress a channel that represents high-frequency noise if the input image's overall spectral descriptor indicates a high noise level. It acts as a complementary mechanism to AFPM: while AFPM provides **local, position-aware** control within the frequency map, SCA provides **global, channel-wise** control based on the image's overall spectral characteristics.\n\n---\n\n### **Response to Weakness 3: Potential boundary artifacts from hard partitioning**\n\nThis is an important and valid concern for any patch-based method. We argue this is not a significant issue in our framework for two key reasons.\n\n1.  **The Global Nature of IFFT:** The modulation occurs in the frequency domain, but artifacts are perceived in the spatial domain. The final IFFT is a global integration operation. Every pixel in the output spatial feature map is an integral of all components across the entire frequency map. This inherent integration process naturally smooths out potential sharp discontinuities between adjacent frequency patches, preventing visible blocking artifacts.\n\n2.  **Experimental Validation and Trade-off Analysis:** Empirically, our extensive results show no such artifacts. To investigate this further, we conducted a new experiment using **overlapping patches (50% overlap)**. While this slightly improved PSNR on Deblur-RAW from 44.73 dB to **44.87 dB** (0.14dB higher), it significantly increased inference time from 75.36ms to **93.46ms (~24% slower)**. This reveals a clear trade-off between a marginal performance gain and a substantial loss in efficiency. Our non-overlapping design achieves a superior balance, which aligns with the core \"Efficient\" goal of our work. We believe a discussion of this trade-off analysis will be a valuable addition to our paper, and we plan to incorporate it to justify our design choice.\n---\nOnce again, we thank the reviewer for their time and constructive feedback, which have been instrumental in improving our work."}, {"review_id": "R1Az2CYdW1", "reviewer": "Reviewer_EG1v", "summary": "In 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.", "questions": "1. 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.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "We are deeply grateful to the reviewer for their positive assessment and valuable, constructive feedback. We appreciate the recognition of our AFPM module's novelty, our model's efficiency, and its interpretability. We have addressed the suggestions below and will incorporate these new results and discussions into our final manuscript.\n\n---\n### **Response to Weakness 1 & Question 1: Ablation on Frequency Domain Design**\n\nWe thank the reviewer for this excellent suggestion to ensure our performance gains stem from structural innovation rather than model capacity. We have conducted a crucial control experiment to explicitly assess the contribution of our frequency domain designs.\n\n1.  **Performance of a Purely Spatial Version:** We created a \"pure spatial-domain\" version of our model, named FrENetSpatial, by completely removing all frequency domain operations.\n\n2.  **On Comparing with an Iso-Parametric NAFNet:** Regarding the suggestion to compare with an iso-parametric NAFNet, we believe our existing results already provide a clear conclusion. As shown in our main paper (Table 1), our **FrENet (19.76M Params, 2.22G MACs) achieves a PSNR of 44.73 dB**, while **NAFNet64 (67.79M Params, 3.96G MACs) achieves 40.35 dB**. Our model obtains better performance with fewer parameters and MACs. This result indicates that our performance gains are driven by architectural efficiency rather than increased capacity.\n\nThe results of our new ablation study on the Deblur-RAW dataset are as follows:\n\n**Table: Ablation Study on Frequency-Domain Design.**\n| Method | Freq. Module | PSNR (dB) | SSIM |\n| :--- | :---: | :---: | :---: | \n| FrENetSpatial | No | 41.89 | 0.9894 |\n| **FrENet (Ours)** | **Yes** | **44.73** | **0.9931** | \n\nThis new result provides the final piece of evidence. Comparing FrENet with FrENetSpatial, our frequency domain design provides a performance gain of +2.84 dB. This, combined with our results against NAFNet64, robustly demonstrates that our model's performance is fundamentally driven by our novel and efficient frequency-domain architecture.\n\n---\n### **Response to Weakness 2 & Question 3: Quantifying Runtime Cost**\n\nWe agree that quantifying the actual runtime cost is essential. While MACs provide a hardware-agnostic measure, real-world inference speed is crucial. We have benchmarked the end-to-end inference time on full-resolution RAW images on a single NVIDIA RTX 5880 Ada GPU.\n\n**Table: Efficiency Comparison on Full-Resolution RAW Images.**\n| Method | LoFormer-L [26] | FFTFormer [18] | Restormer [46] | **FrENet (Ours)** |\n| :--- | :---: | :---: | :---: | :---: |\n| **Runtime (ms)** | 222.49 | 222.56 | 102.95 | **75.36** |\n\nThe results clearly show that despite its reliance on FFT/IFFT, FrENet's highly efficient architecture makes it significantly faster (1.36x to 3x) than other high-performing methods in a practical, high-resolution scenario. This confirms that the FFT/IFFT operations do not create a performance bottleneck in our framework.\n\n---\n### **Response to Question 2: Generalization to Other RAW Restoration Tasks**\n\nWe thank the reviewer for this valuable suggestion. We agree that the design of AFPM is indeed versatile and that its core principle—modulating frequencies based on their position—is applicable to other RAW-to-RAW restoration tasks where degradations have distinct frequency-domain patterns (e.g., high-frequency noise or demosaicing artifacts). During the rebuttal phase, we have tried to applied our method to RAW-to-RAW denoising, and the training has not completed during the limited rebuttal time. As for demosaicing, current methods mainly focus on RAW-to-sRGB task, where our FrENet has the potential to be effective in RAW processing, and the subsequent RAW-to-sRGB stages may require more specialized design.\n\n---\nOnce again, we express our sincere gratitude for the reviewer's insightful feedback, which has been instrumental in enhancing the clarity and impact of our manuscript."}]

Author rebuttal:

Meta-review: The paper describes a new architecture called Frequency Enhanced Network (FrENet) for image deblurring from a RAW sRGB image. Deblurring occurs at the frequency domain, and the authors introduce a novel module called Adaptive Frequency Positional Modulation (AFPM) that dynamically adjusts frequency components. Frequency domain skip connections are used to preserve high-frequency details. Experiments show FrENet producing SOTA results.

Pros: The idea of frequency domain filtering that involves partitioning the frequency spectrum into patches and applying position-dependent modulation is new. The overall design is well-engineered, with informative ablation studies. Results were shown to be SOTA.

Cons: Evaluation could be more extensive, with additional comparisons done with recent work such as StripFormer, Hybrid Attention Transformer, RawIR, and ELMFormer. Ablation studies, while informative, could also be more extensive, for example, providing a cost breakdown.

The general agreement on novelty and the SOTA results are the main reasons for acceptance.

The reviewers appear to be happy with the author rebuttals that clarified many of the issues the reviewers have. For example, a reviewer thought the analysis of the contributions of APFM and SCA modules independently is missing in the ablation studies. However, the authors pointed out this analysis was done; it was the labeling in a table that was not explicit enough. Also, the authors gave results that compare against StripFormer, but was unable to do so for the rest due to unavailability of training code.
Decision: Accept (poster)