RefDeblur: Blind Motion Deblurring with Self-Generated Reference Image
Authors: Insoo Kim
Geonseok Seo
Hyong-Euk Lee
Jinwoo Shin
Venue: Accepted by TMLR ()

Abstract: The challenge of blind motion deblurring is often tackled via two distinct paradigms: kernel-based and kernel-free methods. Each deblurring method provides inherent strengths. Kernel-based methods facilitate generating texture-detailed sharp images by closely aligning with the blurring process. In contrast, kernel-free methods are more effective in handling complex blur patterns. Building upon these complementary benefits, we propose a hybrid framework that decomposes a non-uniform deblurring task into two simpler tasks: a uniform kernel estimation, managed by our kernel-based method, and error prediction, handled by our kernel-free method. Our kernel-based method serves to generate a reference image with realistic texture details while our kernel-free model refines the reference image by correcting residual errors with preserving texture details. To efficiently build our kernel-based model, we consider the logarithmic fourier space that facilitates estimating a blur kernel easier by simplifying the relationship between blur and sharp samples. Furthermore, the regime under using a texture-detailed reference image allows for reducing the size of our kernel-free model without compromising performance. As a result, the proposed method achieves remarkable performance on several datasets such as RealBlur, RSBlur and GoPro, and comparable performance to state-of-the-art methods with a 75% reduction in computational costs.

Published in Transactions on Machine Learning Research (09/2025)
RefDeblur: Blind Motion Deblurring with Self-Generater Reference Image
Insoo Kim
nasoll.iim@sammunn...
AT Center, Somsung Electronics Korea Advanced Institute of Science & Technology (KAIST)
Geonscok Sco
gansukkseco@sssmmsung..omm com
AI Center, Samsung Electronics Seoul National University (SNU)
Hyong-Eus Lee Al Center, Samsung Electronics
kgjongeuk.leed@@amssnng.comm
Jinwoo Shin
jinwoos kc.
Korea Advanced Institute of Science & Technology (KAIST)
Reviewed on OpenReview: https: // openreview. net/ forum? id= Nywwww 7uz tw
Abstract
The challenge of blind motion deblurring often tackled via two distinet paradigms: kernel-basod and kernel-free methods. Each deblurring method provides inherent strengths. Kernel-based methods facilitate generating lexture-detailed sharp images by closely aligning with the blurring process. In contrast, kernel-free methods are more effective in handling complex blur patterns. Building upon these complementary benefits, framework that decomposes a non-uniform deblurring task into two simpler tasks: a uniform kernel estimation, managed by our kernel-based method, and error prediction, handled by our kernel-free method. Our kernel-basee method serves to generate a reference image with realistic texture details while our kernel-free model refines the reference image by correcting residual errors with preserving texture details. To efficiently build our kernel-based model, consider the logarithmic fourier space that facilitates estimating blur kernel easier by simplifying the relationship between blur and sharp samples. Furthermore, the regime under using texture-detailed reference image allows for reducing the size of our kernel-free model without compromising performance. As a result, the proposed method achieves remark- able performance on several datasets such as RealBlur, RSBlur and GoPro, and comparable performance to stale-of-the-art methods with a 75% reduction in computational costs.
propose hybrid
Introduction
Motion blur images often oceur low-light environments and scenes involving high-speed movements. They are caused by camera motion and object movement in the long exposure time. In low-light conditions, using long exposure setting reduces noise but results in substantial blur. The goal of blind image deblurring is to restore sharp images from blur ones.
In carlier years, various deep methods have been devoted to investigating accurate blur kernel estima- tion (Schuler et al., 2015; Chakrabarti, 2016; Gong et al., 2017; Tran et al., 2021; Zhang el al., 2021; Carbajal et al., 2023). These kernel-based techniques prioritize estimating the blur kernels, which are then used in the dcconvolution process (Fish et al., 1995; Krishnan & Fergus, 2009) to generate sharp images. Although they show promising results in certain cases, their performance is not acceptable for real-world scenarios. In fact, there some drawbacks to kernel-based based methods. First, they train with synthesized motion blur images

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aa)
(b)
(c)
Figure 1: Visual comparison of the reference images: (a) Blur image, (b) NAFNet (Chen et al., 2022), and (c) Our kernel-based model with x = 0.01.
that may not hold in practice. Second, estimating blur kernel for every pixel is huge ill-posed problem. Therefore, they may lead to unfavorable ringing artifacts due to inaccurate kernels, especially in noise and saturation pixels (Yuan et al., 2007; Whyte et al., 2014). Third, finding accurate per-pixel kernels may lead to increasing model size, and they mainly come with a time-consuming deconvolution procedure (Fish et al., 1995). In recent years, kernel-free methods have become more popular in motion deblurring tasks (Tao et al., 2018; Kupyn et. al., 2018; 2019; Zamir et al., 2021; Cho et al., 2021; Mao et al., 2023; Nah et al., 2022; Tu et al., 2022; Zamir et al., 2022; Wang et al., 2022; Chen et al., 2022; Tsai et al., 2022; Kong et al., 2023; Li et al., 2023; Kim et al., 2024; 2025), exhibiting strong results without relying on blur kernels. However, they are primarily influenced by the network design (Ronneberger et al., 2015; He et al., 2016; Dosovitskly et al., 2021; Tolstikhin et al., 2021) and capacity (He et al., 2016; Zagoruyko & Komodakis, 2016; Tan & Le, 2019).
While the kernel-based method can produce better texture-detailed sharp images over the kernel-free met hod by closely following the blurring process, the kernel-free method is beneficial for effectively managing complex blur patterns. Inspired by this, we propose hybrid scheme that combines the strengths of kernel-based and kernel-free deblurring methods. Specifically, we decompose a non-uniform deblurring task into a uniform kernel estimation (e-g., managed by our kernel-based method) and error prediction tasks (e-g., managed by our kernel-free method). Our kernel-based model is responsible for estimating blur kernel, which is used to generate a texturo-detailed reference image. Then, our kernel-free model climinates some errors in the self-generated reference images while preserving the remaining texture details. According to Reference-based ReppltResoottion (RefSR) tasks (Zhang et al., 2019; Jiang et al., 2021), they suggest using an additional high-resolution reference image to benefit from meaningful details of the reference image. Namely, leveraging the additional genfraeed reference image is a key strategy of our method to recover the realistic details of the deblurred image. In our experiments, our kernel-based method as shown in Fig. 1 (c) is preferred rather than the artifact-free reference image generated from the recent kernel-free methods as shown in Fig. 1 (b). This is because high texture-detailed reference image can be contributed to restoring the realistic details for deblurring tasks.
found that the rich-detailed reference image generated from
To build an efficient kernel-based model for reference training, we pay attention to the Logarithmic Fourier Transform (LogFT) (Brigham & Morrow, 1967; Childers et al., 1977). Our intuition that the logarithmic fourier space allows for simplifying the blur-sharp relation from complicated deconvolution to subtraction so that lcarning uniform kernels becomes casier that equipped with a texture-detailed reference image, we can achieve a scaled-down version of our kernel-free model, whose performance is still comparable to that of state-of-the-art methods (Tu et al., 2022; Wang et al., 2022; Chen et al., 2022; Tsai et al., 2022) as shown in Table 1. Extensive experiments have been conducted to demonstrate the superiority of the proposed method under various datasets such as RealBlur (Rim et al., 2020), GoPro (Nah et al., 2017), and RSBlur (Rim et al., 2022) datasets.
use light- kernel-basod model. Furthermore,
2 Related Works
Kernel-based methods. Kernel-based methods (Schuler et al., 2015; Chakrabarti, 2016; Sun et al., 2015) estimate per-pixel kernels and subsequently apply existing non-blind deblurring methods (Fish et al., 1995; Krishnan & Fergus, 2009) to produce sharp images. To simplify blur kernel estimation, a motion flow esti- mation method (Gong et al., 2017) explored to generate motion flow maps. Similarly, MotionETR, (Zhang et al., 2021) is developed to estimate motion offsets, which are subsequently used to reconstruct sharp images.
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MEPOOE
PLTSE
(a)
(b)
(c)
(d)
Figure 2: Visual results on the proposed method: (a) Blur image (imput), (b) Estimated blur image by (6), (c) Estimated reference image by (5), and (d) Final deblurred image by (8). Our kernel-baad based model is trained with x = 0.01.
J-MKPD (Carbajal et al., 2023) proposes an efficient solution by introducing a set of pixcl-shared motion bases along with pixel-wise mixing coefficients to accurately estimate per-pixel kernels. Most kernel-based methods struggle to use them in real-world scenarios. This is because they are mainly trained with synthetic data to learn blur kernels or motions. On the other hand, our sernel-based method not only learns blur kernels from realistic blur-sharp pair data (Rim et al., 2020; 2022), but also plays role to provide a reference image to our kernel-free method for better motion deblurring.
Kernel-free methods. The kernel-free methods (Nah et al., 2017; Cho et al., 2021; Chen et al., 2022; Kong et al., 2023; Cui et al., 2024) are based on image-to-image translation tasks, mapping blur images to sharp ones in realistic blur datasets (Rim et al., 2020; 2022). Notably, earlier works (Tao et al., 2018; Nah et al., 2017) introduce a coarsc-to-fine strategy that progressively reconstructs sharp images, starting from low-resolution to high-resolution images. In order to implement this strategy more efficiently, some literature (Zamir et al., 2021; Cho et al., 2021; Mao et al., 2023) employs multi-input and multi-output U-Net architectures (Ronneberger et al., 2015). Transformer- et al., 2022; Tsai et al., 2022; Kong et al., 2023) are also developed, aiming to enhance image detail recovery by addressing the quadratic complexity of self-attention mechanisms in innovative ways. Additionally, several studies focus on network architecture design to effectively capture both local and global information (Zamir et al., 2021; Tu et al., 2022; Zamir et al., 2022; Li et al., 2023). Recently, prior-based methods (Li et al., 2022; Fang et al., 2023; Kim al., 2024) have been investigated to further improve deblurring performance. NAFNet (Chen et al., 2022) introduces
deblurring techniques (Zamir et al., 2022; Wang
efficient network architecture that utilizes simplified channel attention and simple gates. This scheme achieves state-of-the-art performance the single image deblurring with small computational costs. Our method is based on this efficient kernel-free method but additionally employs a self-generaedd reference image, which enables us to further reduce computational costs while preserving state-of-tho-art performance.
3 Reference- Based Blind Motion Deblurring
In this section, we present a new deblurring approach that leverages both kernel-based and kernel-free techniques. We begin by discussing a new approach to handling non-uniform deblurring tasks in Section 3.1. Next, we introduce a logarithmic fourier kernel estimator that yields a reference image in Section 3.2. Scction 3.3 covers a reference- based kernel-free method, and finally, we present our hybrid method with implementation details and discussions in Section 3.4.
3.1 A new approach to handling non-uniform deblurring
The usage of additional reference images, which aid in restoring the texture details the image, is a key component of our deblurring scheme. In essence, the image deblurring approaches (Zamir et al., 2021; Cho et al., 2021; Chen et al., 2022) aim to primarily recover the texture details of the image only from a blur image. On the other hand, our hybrid scheme focuses on recovering the texture details of the image from
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Psnudl-Sharp
Image
LogFT IFTEx;
Blur Image
Estimated
Concatenation
Blur Image
Deblumeed
Bernel-Bssd
Madel
Deconvelution
Reference Image
Kernel-Free Model
LogFT
Kernel- Based Method
Kernel-
Frree--Free Free Method
Figure Network architecture overview. Our network architecture consists of two components: kernel- based model and kernel-free model. We use U-Net (Ronneberger et al., 2015) as our kernel-based model. NAFNet (Chen et al., 2022) is adopted for our kernel-free model. LogFTT indicates the logarithmic fourier transform and TF'Exx means the inverse logarithmic fourier transform.
both a blur and a self-generated reference image. To this end, we first investigate a kernel-based method that follows a blurring process to generate a rich-detailed reference image inevitably with some artifacts. Then, our kernel-free model concentrates on getting rid of the artifacts in such reference images while preserving the remaining texture details.
In the case of uniform deblurring, given pair of blur image y and uniform blur kernel k, the sharp image is reconstructed by using the following deconvolution process
y 3 k
where is deconvolution operation. To extend to non-uniform deblurring, let the total per-pixel error Ex + em represent the kernel type error eμ (residual blurs) when the motion blur is not uniform, i.e., non-uniform blur, and the kernel model error em (artifacts) when the estimated kernel is not accurate. Then, we approximate a non-uniform deblurred image I,
ă = (y O + + = (y 2 (ee AA)) t
= k + y Ak c = y 1 + cm + cx
= y
7, +e,
(2)
where ž, is the reference image and Ak = - k is the difference between real and estimated kernels. Since the kernel model error mathematically contains the blur image in (2), we consider kernel-free model fe that takes two inputs, e.g., reference image Σ, and blur image to handle two types of errors, e.g. e e= fo y). According to (2), the non-uniform deblurring problem (i.e., difficult task) decomposed into uniform kernel estimation (reference estimation) and error prediction, i.e., two simpler tasks, Namely, the non-uniform deblurring can be viewed as reference-basod deblurring with a self-generatod reference image. To simplify the deconvolution process, we first consider Discrete Fourier Transform (DFT) of sample image y follows:
N-1
Y(w) = Σ y(n)e- T==
(3)
where v(n) is realevalued number at a pixel and Y(w) denotes complex-valued number at frequency w. We remark that discrete fourier transform F naturally converts deconvolution into division, i.c., O k 5 F(w)/((k) (Brigham & Morrow, 1967). To further simplify the relationship between two fourier samples, we introduce a logarithmic operation in the fourier domain, leading to the logarithmic fourier transform Fr of the sample images, and then we obtain the relation y O = Fr (v) - Fk(k) (Childers et al., 1977).
Let X (2) and Y Fy( from a dataset logarithmic fourier operator Ft.- Let KL (k) be blur kernel in the logarithmic fourier space. We are
{(x, v)} be sharp and blur samples by the
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(a)
(b)
(c)
(d)
Figure 4: Reference comparison results: (a) Blur image (input), (b) Reference image with À 1.0, (c) Reference image with À 0.01, and (d) Reference image with X = 0.001.
interested in estimating kernel KL modeled by kernel-based model ge that maps from (x", K: take two inputs, e-g., blur Y and sharp image XL to facilitate estimating accurate kernels if some literature considers image prior terms (Krishnan et al., 2011) for similar reasons. As the ground-truth sharp image is only valid in the training phase, we instead use the pseudo-sharp image by an off the-hhh-hef image deblurring model in the evaluation phase. Then, with the estimated kernel KL, the reference image ž, is generated by using simple subtraction (c.g., deconvolution) and inverse logarithmic fourier transform FI i.., ž, is the reference image estimated from our kernel-based model Ie and is the total per-pixel error obtained by our kernel-free model fo- The whole network architecture is illustrated in Fig. 3.
-K-- shown in Fig. 2 (c). Finally, we obtain the deblurred image ă ž, where I,
3.2 Logarithmic fourier kernel estimation
In this section, two key schemes are presented to reduce the nature of ill-posedness (make it more accurate) and train a kernel estimator Iø under a light-weight. network architecture (make more efficient): (1) we utilize the logarithmic fourier space to learn a blur kernel more easily (Kim et al., 2024) and (2) we exploit an additional pseudo-sharp image to predict a more accurate kernel. The logarithmic fourier space facilitates learning the blur kernel more easily by simplifying the relationship between blur and sharp samples. Therefore, light-weight network architecture may be sufficient to accommodate the simplified relation. Furthermore, since non-uniformn kernel estimation may require a large network capacity in (Gong et al., 2017; Zhang et al., 2021; Carbajal et al., 2023), we approximate non-uniform kernel estimation to uniform kernel estimation combined with the per-pixel error prediction as discussed in Section 3.1. Specifically, we estimate a uniform kernel under a small-scale network architecture. Then, the per-pixel errors caused by the artifacts and residual blurs are handled in our kernel-free method as explained in Section 3.3. Finally, using an additional pseudo-sharp image restricts the number of feasible kernel solutions, thereby making it simpler to estimate more accurate kernels (i.e., resulting in better referenccs).
To sum up, we aim to train a uniform kernel estimation model to using the logarithmic fourier pair of imput samples, e.g., ground-truth sharp sample "x" and blur sample Y Our kernel-based model gv outputs a logarithmic fourier uniform kernel that is expressed as
K² (x )²)
(4)
With the estimated kernel R4,, we can easily obtain a self- saff-generated reference image as shown in Fig. 2 (c) by
-, = Fr (x', YY)).
(5)
On the other hand, by taking the simple addition in the logarithmic fourier space, we have
= (x + go (x+, y²)),
(6)
where ÿ is estimated blur image shown in Fig. 2 (b). Finally, suggest minimizing the following loss that coincides with the bluring and deblurring procedures to estimate the accurate kernels:
Liernel (vh D)
Σ
d(ğ. v) + Ad(ā,,
D
We observe that. learning our kernel-based model with ground-trutt sharp images results better performance than with pseudo-sharp images.
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where d is some distance divergence in the image domain, e.g., the PSNR loss (Chen et al., 2022) and A is a hyperparameter to control the sharpness of the reference image.
Our reference image generation jointly optimizes deblurring and blurring objectives using a shared blur kernel. When emphasizing the deblurring objective (i.e., À = 1.0), the reference image becomes artifact-fres but lacks texture details, as it tends to prioritize high PSNR performance for the reference image, as shown in Fig. 4 (b). In contrast, prioritizing the blurring objective (i.e., À 0.01) enables more accurate kernel estimation, as the blurring task is significantly easier for the model to learn shared blur kernels than the deblurring task. As result, this strategy facilitates recovering texture details in the reference image, but introduces some artifacts due to the low contribution of the deblurring loss term d( x), as shown in Fig. 4 (c). Further increasing the emphasis on blurring task, e.g., À = 0.001, as shown in Fig. 4 (d), may amplify texture recovery but introduces severe artifacts, ultimately degrading the final deblurring performance compared to the case with 0.01. Further analysis will be explored in Section B.4 of the Appendix.
3.3 Kernel-free method with a reference image
To obtain better performance, it is common to scale up network (He al., 2016; Zagoruyko & Komodakis, 2016; Tan & Le, 2019) or use texture-detailed reference images (Zhang et al., 2019; Jiang et al., 2021). This section introduces free method eliminates the per-pixel artifacts (kernel model error em) and residual blurs (kernel lype error ex) using blur and self-generated reference images, as discussed in Section 3.1. Note that our kernel-free method can climinate the per-pixel residual blurs that are still present in the reference images, especially when dealing with non-uniform blur types.
kernel-free method that exploits such texturo-detailed reference images. Our kernel-
Given two input images, ie., reference image T, and blur image y, the deblurred image To is estimated via our kernel-free model fa follows:
ds = Ir + fo(ār, v),
(8)
where fo(Ør. 3) is the estimated per- per-pixel error è, ty denotes the final deblurred image as shown in Fig. 2 (d) and ãr is the reference image defined by (5). Then, we derive our deblur loss that minimizes the distance between the real sharp and deblureed images by
Laabiur (0:
Σ
d(zo,
(9)
D
where d is some distance or divergence in the image domain, e.g., the PSNR loss (Chen al., 2022).
We highlight that 4,, f2, and PSNR losses are basically specialized to remove noises or artifacts rather than restore texture details. The proposed method is more relevant to use those losses since our kernel-free method is designed for removing artifacts of the texture-detailes reference image.
3.4 Comprehensive discussion on our hybrid deblurring framework
Reference-based blind motion deblurring. In this section, we present the implementation details of our hybrid method. Basically, our hybrid model consists two models: kernel-based and kernel-free models. We adopt NAFNet (Chen et al., 2022) as our kernel-free model. We use U-Net (Ronneberger et al., 2015) as our baseel model?. Since both models take two input images, we simply concatenate them toward the channel dimension to use the original network architectures. In the first stage, train our kernel-based model Oe with the blur and mround-truth sharp images by minimizing (7). In the second stage, freeze kernel-based model Ie and optimize our kernel-free model fo with the reference ã, (from our kernel-based model) and blur images y by using (9). To generate reference images, the ground-truth sharp images are used in the first stage whereas the pseudo-sharp image is used in the second stage: a pseudo-sharp image is obtained by an off-the-shelf image deblurring model, e.g., NAFNet.
The kernel-based model using U-Net achieves comparable performance to NAFNet. However, the training time of NAFNet. longer due its special modules. Therefore, choose U-Net for the kernel-based model.
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EL eUPPOT
RUPPR
EUrPaTI
EUPPPTOI
CUMManO
579 3452
STA 3432A
572 34324
57a 3452
572
Blur
MAXIM-3S Stripformer NAFNet-64
RefDeblur-S
Sharp
Figure 5: Visual comparison results on RealBlur-J (Rim et al., 2020) dataset. We compare our method with saae--of-hheart deblurring methods (Cho al., 2021; Mao et al., 2023; Tu et al., 2022; Chen al., 2022; Tsai et al., 2022). The proposed method shows more texture-detailed, artifact-free, and sharper aspects in the deblurred images.
Discussion on joint training. We further discuss how our hybrid framework behaves under an end-to-end training regime. While an end-to-end training strategy, which involves jointly training both kernel-based and kernel-free models, an intuitive and common approach, we found that its performance (33.15 dB) lags behind that of the two-stage learning strategy (33.38 dB). We believe that the proposed step-by-step training leads more stable convergence and enables each component model (e.g., dernel-based or kernel- free model) be optimally trained, ultimately resulting in better performance, Nevertheless, our hybrid framework trained jointly (33.15 dB) still outperforms the baseline kernel-free model (32.66 dB) under similar computational costs, highlighting the benefits of the hybrid approach itself.
Blur kernel constraints. We do not impose strict physical constraints (e.g., non-negativity and sum-to- one) on the blur kernel, as they may lead to training difficulty. Instead, we apply a [0,1] clamping operation to the generated reference image. This serves two key purposes: (1) ensuring valid pixel ranges for stable input the subsequent kernel-free model, and (2) implicitly regularizing the kernel-based model encourage physically-valid kernel estimation without imposing strict physical constraints.
4 Experiments
4.1
Experimental setup
Dataset and evaluation metrics. We GoPro (Nah al., 2017), RealBlur (Rim al., 2020), and RSBhu (Rim al., 2022) for training and test sets. GoPro contains 2,103 and 1,111 blur-sharp image pairs for training and test sets, respectively. GoPro has been widely used for motion deblurring tasks, but it is synthetic datasct where the blur image is generated by averaging sharp vidco frames captured by a high-speed camera. On the other hand, RealBlur and RSBlur are realistic motion blur datasets where they capture blur and sharp images in the same scene by using the beam splitter. RealBlur consists RealBlur-J (sRGB domain) and RealBlur-R (RAW domain). Each RealBhur type comprises 3,758 and 980 image pairs for training and test sets, respectively. RSBlur dataset contains 8,878 and 3,360 blur-sharp image pairs for training and test sets, respectively. To verify the performance of the proposed method, measure distortion
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Pizza Halt
PataHol
Pazza Half
(a) Blur Image
(b) Reference Image
(c) Final Deblurred Image
Figure Visual results of blur, reference and final deblurred images. Starting from the top row, in or- der, saturation blur in-plane rotation blur (e.g., z-axis rotation) / out-of-plane rotation blur (e-g., y-axis rotation) forward motion blur (e.g., z-axis translation) / object motion blur. The reference images are generated by our kernel-based model trained with À = 0.01.
metrics, c.g., Peak Signal to Noise Ration (PSNR) and Structural SIMilarity (SSIM) Wang et al. (2004), and perceptual metric, e.g., Learned Perceptual Image Patch Similarity (LPIPS) Zhang et al. (2018). To measure model efficiency, operations (MACs) based on the image size of 256 x 256.
compute the number of network parameters and Multiply-ACeumil
Network architecture variants. The default number of blocks of NAFNet (Chen et al., 2022) is 36. NAFNet with 16 widths, 32 widths, and 64 widths are referred to as NAFNet- 16, NAFNet-32, and NAFNet- 64, respectively. Similarly, we refer to our method with 16 widths as RefDeblur-16. To be consistent with the computational cost of NAFNet, we make RefDeblur-T (Tiny) and RefDeblur-S (Small). To cope with large-scale models such Restormer (Zamir al., 2022) and MAXTM (Tu et al., 2022), further increase
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Table 1:
The effect of the proposed method compared against NAFNet (Chen et al., 2022). The proposed
method yiclds comparable performance with almost quarter of the computational costs. Under similar
computational costs, our method out perforrrms NAFNet with respect to several scales of network architectures.
RealBlur-
Methods
# Params
GMACs
PSNR f
SSIM
NAFNet-16 (Chen al., 2022)
4.00
4.23
31.64
0.912
RefDeblur-16 (ours)
8.39
9.65
32.37
0.921
NAFNet-32 (Chen et al., 2022)
16.00
16.25
32.17
0.920
Re(Deblur-T (ours)
12.40
16.33
32.70
0.927
NAFNet-64 (Chen et al., 2022)
63.50
63.64
32.66
0.929
RefDeblur-S (ours)
54.20
62.61
33.38
0.938
RefDeblur-B (ours)
123.50
136.88
33.81
0.941
the model sizes, which is referred to as RefDeblur-B (Big). Our network variants are described in details in Section A of Appendix.
Implementation details. We train with RealBlur (Rim et al., 2020), GoPro (Nah et al., 2017), and RSBlur (Rim et al., 2022) randomly cropped by 256 256. We train RefDeblur-16 up to 1, 000 epochs (batch size 16) for RealBlur, our RefDeblur variants (T //S/ B) up to 2, 000 epochs (batch size 32) for RealBlur, our RefDeblur variants (S B) up to 1, 0000 epochs (batch size 32) for RSBhur and our RefDeblur-B up 12, 000 epochs (batch size 64) for GoPro. Our kernel-free model optimized by the AdamW (Loshchilov & Hutter, 2019) algorithm (31 0.9, B2 0.9 and weight decay 1e-3) with the cosine annealing schedule (le-3 to le=") gradually reduced for total iteralions of each dalaset. We oplimize our kernel-based model using the Adam algorithm (31 = 0.9, 62 = 0.9 and weight decay 0) with the step schedule (initial learning rate le le-4) by decrcasing the factor of 0.5 every 200 epochs for RealBlur and RSBlur dataset (up to 1, 000 epochs), and every 600 epochs for GoPro dataset (up to 3, 000 epochs). We employ the hyperparameter of ours as 0.01 and use RealBlur-J for all ablation studies unless otherwise specified.
4.2 Reference images for various blur types
We examine that our pernel-based method produces reasonable reference images on several non-unifore blur types such as saturation, in-plane camera rotation (c.g., z-axis rotation), out-of-planc camera rotation (c.g.. y-axis rotation), forward motion (e.g., z-axis translation) and object motion blurs. To generate the reference images from the various non-uniform blur types, we collect those blur images from LOL-Blur (Zhou et al., 2022), Heterogeneous Motion Blur (Gong et al., 2017), GoPro (Nah et al., 2017), and real-world blur images captured by Samsung Galaxy Note 20 Ultra. As shown in Fig. 6 (b), under various non-uniform blur types, the reference images represent the sharpened visual results with some entailing artifacts. As discussed in Scction 1, our reference images cxhibit more ringing artifacts, especially in saturation and low-light (c.g., noisy) regions where the blur kernels become less accurate. Nevertheless, all those artifacts are removed by our fereel-free model as shown in Fig. (c).
4.3 Effects of reference- based image deblurring
Model efficiency. This experiment aims to show the model efficiency of our method, compared to our bascline, c.g., NAFNet (Chen et al., 2022). We train and evaluate with RealBhur- dataset We measure PSNR and SSIM for model performance while the number of network parameters and MACs are measured for model efficiency. Note that the computational cost of FFT operation is negligibly minor so it is not considered in MACs. As shown in Table 1, the performance of our RefDeblur-T T is comparable to that of NAFNet-64, almost with a quarter of the computational costs. Furthermore, our method improves PSNR from 32.66 to 33.38 dB with similar MACs (see the results of NAFNet-64 and Re/Deblur-S).
datasset (Rim et al., 2020).
Quantitative and qualitative results. We first present the quantitative results on RealBlur-J (Rim et al., 2020). The experimental results are summarized in Table Our RefDeblur-B achieves the best performance, reaching 33.81 dB@PSNR, outperforming previous methods by significant performance gap.
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Table Comparison results on RealBlur-J and datasets (Rim et al., 2020).
RealBlur-J
RealBlur-R
Methods
GMACss
PSNRT
SSIM LPIPS}
PSNRT SSIMT LPIPSL
MIMO-UNet F (Cho ct al., 2021)
154.41
31.92
0.919
0.109
MSDI-Net (Li et al., 2022)
336.43
32.35
0.923
MPRNet (Zamir et al., 2021)
777.01
31.76
0.922
39.31
0.972
Stripformer (Tsai et al., 2022)
169.89
32.48
0.929
0.083
39.84
0.974
0.028
MAXIM-3S (Tu al., 2022)
169.50
32.84
0.935
0.089
39.45
0.962
0.042
FFTFormer (Kong et al., 2023)
131.45
32.62
0.932
0.089
40.20
0.973
0.033
GRL-B (Li et al., 2023)
1285.28
32.82
0.932
40.20
0.974
UFPNet (Fang al., 2023)
243.33
33.35
0.934
0.088
40.61
0.974
0.034
CLIMO-UNet (Mao et al., 2023)
80.21
32.65
0.931
0.100
40.01
0.972
0.044
SegDeblur-I. (Kim et al., 2024)
62.68
32.95
0.934
0.088
40.21
0.975
0.032
NAFNet-64 (Chen et al., 2022)
63.64
32.66
0.928
0.089
39.59
0.973
0.045
RefDeblur-S (ours)
62.61
33.38
0.938
0.083
40.70
0.976
0.031
RefDeblur-B (ours)
136.88
33.81
0.941
0.079
41.16
0.978
0.027
Table 3: Comparison results
RSBlur (Rim et al., 2022)
dataset. The best results are indicated in bold.
RSBlur
Methods
GMACs
PSNR
SSIM f
SRN-Deblur (Tao al., 2018)
1434.82
32.53
0.840
COMO--Net-att (Cho et al., 2021)
154.41
33.37
0.856
MPRNet (Zamir et al., 2021)
777.01
33.61
0.861
Restormer (Zamir et al., 2022)
141.00
33.69
0.863
Uformer-B B (Wang et al., 2022)
89.50
33.98
0.866
IRNeXt (Cui et al., 2023)
114.79
34.08
0.869
ConvIR-L (Cui et al., 2024)
129.34
34.06
0.868
NAFNet-64 (Chen et al., 2022)
63.64
33.65
0.862
ReiDeblur-S (ours)
62.61
34.01
0.867
RefDeblur-B (ours)
136.88
34.24
0.871
Furthermore, method can be interpreted as prior-based deblurring method due to using an additional reference image, we compare it with other prior-based deblurring methods such as MSDI-Net (Li et al., 2022), UFPNet (Fang et al., 2023), and SegDeblur (Kim et al., 2024). We observe that our RefDeblur-S achieves superior deblurring performance compared to the prior- mothods, while requiring small computational cost. (62.61 GMACs). Moreover, our proposed two-stage pipeline achieves superior perceptual quality (i.e., better LPIPS scores) compared the other methods. This suggests that the reference image utilized in our method plays crucial role in enhancing perceptual quality, thereby leading to better LPIPS scores. As illustrated in Fig. 5, our method shows more texture-detailes and sharper images compared to state-of- the-art deblurring methods. Furthermore, we present qualitative results on real-world scenarios as shown in Fig. 7 of Appendix.
Results on RAW blur images. The realistic deblurring process defined by (1) may not hold in the sRGB domain since the acquired RAW blur images (e.g., RealBlur-R (Rim et al., 2020)) are non-linearly transformed sRGB blur images (e.g,, RealBlur-J (Rim et al., 2020)) via Image Signal Processing (ISP) pipelines (Rim et al., 2022). In other words, the RAW domain is more relevant domain to predict more accurate blur kernels. This implies that our method can produce higher-quality reference images in the RAA domain, leading to further performance improvement. To verify this, we train and evaluate with RealBlur- R. As shown in Table 2, our method significantly improves PSNR performance from 39.59 (NAFNet-64) to 40.70 dB (RefDeblur-S) under similar computational costs. Interestingly, the performance gap in the RAW domain, i.c., RealBlur-R (1.11 dB) between NAFNet-64 and RefDeblur-S noticcably higher than the gap in the sRGB domain, i.c., RealBlur-J (0.72 dB). Since the blur kernels are more accurately estimated in the RAW domain, the RAW domain is the best demonstration of the effectiveness of our method.
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Table 4:
Comparison results on GoPro (Nah et al., 2017) dataset. Restoration-based and diffusion-based
methods are grouped separatoly. The best results are indicated in bold.
GoPro
Methods
GMACs
PSNR t
SSIM
LPIPS &
Restoration- based Methods
SRN-DeblurNet (Tao et al., 2018)
1434.82
30.26
0.932
DeblurGAN-v2 (Kupyn et al., 2019)
411.34
29.55
0.934
MPRNet (Zamir et al., 2021)
777.01
32.66
0.959
0.089
MIMO-UNet (Cho et al., 2021)
154.41
32.45
0.957
0.091
Restormer (Zamir et al., 2022)
141.00
32.92
0.961
0.084
Uformer-B (Wang et al.; 2022)
89.50
32.97
0.967
0.087
Stripformer (Tsai et al., 2022)
169.89
33.08
0.962
0.078
MAXIM-3S (Tu et al., 2022)
169.50
32.86
0.961
0.088
IRNeXt (Cui et al., 2023)
114.79
33.16
0.962
0.084
ConvIR-L (Cui ct al., 2024)
129.34
33.28
0.963
0.083
NAFNet-64 (Chen et al., 2022)
63.64
33.69
0.967
0.079
NAFNet-96 (Chen al., 2022)
141.72
33.66
0.970
0.078
RefDeblur-S (ours)
62.61
33.98
0.972
0.075
RefDeblur-B (ours)
136.88
34.10
0.973
0.073
Diffusion-based Methods
DvSR-SA (Whang et al., 2022)
33.23
0.963
0.078
Hi-Diff (Chen et al., 2023)
142.62
33.33
0.964
0.079
DifIR (Xia et al., 2023)
112.75
33.20
0.963
0.078
4.4 Comparison to kernel-free free methods
Performance on realistic high-resolution blur dataset. To compare with the recent works under the high-resolution (around 1920 1200) realistic blur dataset, we train and evaluate with RSBlur (Rim et al., 2022). As shown in Table 3, our RefDeblur-B achieves the best performance over the previous methods. In particular, our RefDeblur-S improves PSNR performance of NAFNet-64 from 33.65 to 34.01 dB under similar computational costs.
We evaluate our method against recent deblurring methods on GoPro dataset to demonstrate its effectiveness in terms performance and computational efficiency. As presented Table 4, our ReiDeblur-S outperforms all competing restoration-basee methods on the GoPro dataset. It achieves state-of-the-art results across all metrics. Crucially, our RefDeblur-S demonstrates exceptional efficiency, using only 62.61 GMACs, which is substantially lower than most competitive baselines. Furthermore, shown in Table 4, we compare our method with recent diffusion-basse deblurring models such as DvSR- SA (Whang et al., 2022), Hi-Diff (Chen et al., 2023), and DiffIR (Xia et al., 2023). Despite the strong LPIPS performance of diffusion- based methods, our RefDeblurS attains a better LPIPS (0.075), along with higher PSNR (33.98 dB) and SSTM (0.972) than all diffusion-based baselines. Unlike diffusion models that require expensive iterative sampling, our ReiDeblur-S operates once with lower computational cost. These results demonstrate that our hybrid framework offers strong performance in both perceptual quality and efficiency, making it well-suited for practical usage.
Results on GoPro.
5 Ablation study
Analyzing the role of each training stage. In this section, explore the effectiveness of our two-stage training. As described in Table 5, we begin our ablation study with the pscudo-sharp image generated from NAFNet-16 (Chen et al., 2022), which yields a PSNR of 31.82 dB. When we introduce reference training, i.., our kernel-based method, to explicitly generate a reference image, we observe a drop in PSNR to 30.16 dB due the presonce of artifacts in the reference image. However, the reference image still contains rich texture information as exemplified in Fig. 1 (c), which not captured in the pseudo-sharp image, as exemplified in Fig. 1 (b). In the final stage, applying artifact-free training, i.e. our kernel-free method
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Table 5: Ablation study on the effectiveness of our hybrid deblurring framework on the GoPro dataset.
Pseudo-Sharp Image
Reference Training
7
Artifact-Free Training
PSNR (dB)
31.82
30.16
33.20
Table 6: Comparison results of lightweight models on the GoPro dataset. Method
GMACss
PSNR
SSIM t
NAFNet-32 (Chen et al., 2022)
16.25
32.85
0.960
FFTFormer-16 16 (Kong et al., 2023)
16.41
32.81
0.959
RefDeblur-TT (ours)
16.33
33.20
0.967
Table 7: Comparison results between our hybrid deblurring framework and a spatially-variant kernel esti- mation across different network capacities.
Method
RefDeblur-T
RefDeblur-S
RefDeblur-B
Spatially- Variant Kernel Estimation Paatially-Variant
31.24
31.42
31.56
Hybrid Deblurring Framework (ours)
32.70
33.38
33.81
with the reference image, allows the model to eliminate artifacts while retaining fine details. This leads to a significant PSNR improvement to 33.20 dB, notably exceeding the performance of NAFNet-32 (32.85 dB) under the same computational cost. This result clearly demonstrates the effectiveness of our two-stage design, where leveraging a high-detail but flawed reference image leads to better final deblurring results when performing artifact-free training.
Efficient deblurring training. To validate the effectiveness of our referenco-basee design in resource- constrained settings, we evaluate RefDeblur-T (16 GMACs) on the GoPro dalaset (Nah et. al., 2017) and compare it against other lightweight baselines such as NAFNet-32 (Chen et al., 2022) and FFTFormer- 16 (Kong et al., 2023). As shown in Table 6, our method achieves approximately 0.4 dB higher PSNR than NAFNet-32 and FFT 16, demonstrating that the proposed peereence-guided deblurring is particularly effective when model capacity is constrained (e.g., small). These results highlight the practical advantage of our framework in efficient deblurring scenarios.
Spatially-variant kernel estimation vs. hybrid deblurring framework. While the kernel-based deblurring, aligned with physical blur modeling, produces texture-rich images, it struggles with accurate spatially-variant kernel estimation due to the training difficulty. Our hybrid framework leverages both: it first uses a kernel-based deblurring, inheriting the capacily to render rich-detailed reference image with some errors. Then, it employs a kernel-free method, specialized in correcting intricate and spatially-variant remaining crrors, to effectively remove complex blur degradations. As demonstrated in Table 7, our proposod hybrid deblurring framework demonstrates scalability with increased network capacity and achieves superior performance compared to spatially-variant kernel estimation.
6 Conclusions
In this paper, we propose reference-based motion deblurring scheme that combines kernel-based and kernel- free methods. Our hybrid deblurring scheme combines the strengths of two methods: it initiates with kernel-based method specialized in restoring high-fidelity details, and then incorporates a kernel-free method specialized in correcting intricate and spatially-variant remaining errors, to effectively removing complex blur dogradations. We demonstrate that such hybrid stratogy reduces computational costs by 75%, compared to stale-of-the-ar methods. The proposed method can be exlended to other deblurring lasks such as video deblurring (Wang et al., 2019) and defocus deblurring (Abuolaim & Brown, 2020).
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Acknowledgement
This work was supported by Institute for Information & communications Technology Promotion([ITP) grant funded by the Korea rovernment(MSIT] No.RS-2019-I1190000 Artificial Intelligence Graduate School Sup- port (aArTa(KASST); No.RS-20021-12112666 Artificial Intelligence Innovation Hub; No. RS-2021-00559927,, Global Al Frontier Lab).
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A Implementation Details
Logarithmic operation. For stable training, add 10 when performing the complex logarithmic operation. Furthermore, we stabilize our kernel-based model by initializing the model weights to have the normal distribution (mean : O, standard deviation: 0.02). Hence, the estimated kernel begins by the zero kernel in the logarithmic foruier domain (oquivalent to the identity kernel in the spatial domain).
Network architecture variants. Our hybrid model consists of two models: kernel-based and kernel-free models. We adopt NAFNet (Chen et al., 2022) as our kernel-free model. We use U-Net (Ronneberger al., 2015) as kernel-based model because it empirically shows the consistent performance in varions datascts. Also, we consider the pre-trained NAFNet for generating pseudo-sharp images used in our kernel-based model. The default number of blocks of NAFNet (Chen et al., 2022) is 36 which consists of encoder blocks {2, 2, 20}, middle block {2} and decoder blocks {2, 2, 2, 20}. NAFNet with 16 widths, 32 widths, and 64 widths are referred to as NAFNet-16, NAFNet-32, and NAFNet-64, respectively. Similarly, we refer to our method with widths as RefDeblur-16 as shown in Table To be consistent with the computational cost of NAFNet-32 and 64, we increase the model size of the pseudo-sharp model (NAFNet-16 + NAI-Net-10 or N NAFNet-32), our kernel-based (UNet-16 + UNet-32) and kernel-free model (NAFNet-16 16 + NAFNet-16+ NAFNet-32+) in RefDeblur-16, which are denoted as RefDeblur-T and RefDeblur-S as shown in Table 8, To cope with the large-scale models such as MAXIM (Tu et al., 2022), Restormer (Zamir et al., 2022), and Stripformer (Tssi et al., 2022), we further increase the model size of the pseudo-sharp (NAFNet-32 + NAFNet-64) and kernel-free model (NAFNet-32+ + NAFNet-32++) in RefDeblur-S, which is referred to as RefDeblur-B as shown in Table 8, Here, NAFNet16+ consists of encoder blocks {6, 6, 6, 22), middle block {6} and decoder blocks f6, 6, 6) with 16 widths; NAFNct-32+ consists of encoder blocks {9, 9, 9, 25), middle block {9} and decoder blocks 99 with 32 widths; and NAFNeI-32++ consists of encoder blocks {16, 16, 16, 32), middle block {16} and decoder blocks {16, 16, 16, 16} with 32 widths. Note that we use a variant of U-Net that performs four times of downsampling by stride 2 operations. Overall, our models are summarized in Table 8.
Table 8: Our model variants. We present a detailed description of our individual variants such the sub-models types and the computational costs. The individual GMACs of the sub-models are denoted in brackets.
Model (GMACs)
Our Model
Total GMACs
Total # Params
Pseudo Sharp
Kernel-Bass
Kernel- Free
NAFNet-16
UNet-16
NAFNet-16
ReIDeblur-16
9.65
8.39
(4.23)
(1.16)
(4.26)
NAFNet-16
UNet-32
NAFNet-16+
RefDeblur-T
16.33
12.40
(4.23)
(3.99)
(8.11)
NAFNet-32
UNet-32
NAFNet-32+
ReiDeblurS
62.61
54.20
(16.25)
(3.99)
(42.37)
NAFNet-64
UNet-32
NAFNet-32++
RefDeblur-B
136.88
123.50
(63.64)
(3.99)
(69.25)
B Additional ablation study
B.1 Types of reference images
The goal of this experiment is to verify which types of reference images are appropriate. To generate various types of reference images, the pseudo-sharp images trained with PSNR, perceptual (VGG) (Johnson et al., 2016), and VGG + Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) losses are chosen as reference images. Also, other kernel-based methods such as J-MKPD (Carbajal el al., 2023) and MotionETR (Zhang et al., 2021) are considered as reference generators. Note that we consider the VGG and VGG+GAN losses because they are widcly studied to percoptually recover texture-dctailed images image restoration tasks (Johnson et al., 2016; Ledig et al., 2017). Given those reference images, we train our kernel- free models with them. The PSNR performance of reference and deblurred images are presented in Table 9.
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Although the reference images by VGG and VGG + GAN losses bring in better final deblurring results compared with the result of "No Reference", their performance improvements are relatively small when compared to our method: some texture details from VGG and VGG + GAN may appear in unpleasant visual artifacts rather than realistic details in the image domain (Liang et al., 2022). Furthermore, since the other methods aim for high PSNR performance of the reference image, does not. help produce uexture- detailed reference images. As a reference image, prioritizing the inclusion of high-frequency realistic details even with some artifacts is more crucial for achieving better final deblurring results compared to focusing solely higher reference PSNR (compare the results of "RefDeblur-16f 166 with those of "NAFNet-16 with PS Also, it is proven that the reference image is not always helpful for final deblurring performance (compare the results between "No Reference" and "J-MKPD" and "MotionETTR").
Table 9: Ablation study on types of reference images.
Reference Model
Ref. Deblur
Type
Loss
GMACs
PSNR
PSNR
PSNR
4.23
31.64 31.73
NAFNet-16
VGG (Johnson et al., 2016)
4.23
29.56 31.76
VGG+GAN (Goodfellow et al., 2014)
4.23
30.81
31.80
RefDeblur-16
PSNR
5.39
27.89
32.37
No Reference
31.58
J-MKPD (Carbajal et al., 2023)
56.36
28.88
31.32
MotionETR (Zhang et al., 2021)
55.92
28.04
31.28
B.2 Effects on pseudo-sharg images
In this section, we analyze how the pseudo-sharp images affect the performance of our kernel-based model. As discussed in Section 3.2, we use the pseudo-sharp image because the paired input can restrict the number of feasible kernel solutions, which results in more accurate kernels and better final deblurring results. We adopt training strategy in which the kernel-based model is trained with Ground-Truth (GT) sharp images and evaluated using pseudo-sharp images. This strategy is motivated by two primary reasons. Firstly, its final deblurring performance comparable to training directly with pscudo-sharp images. More importantly, this GT-based training strategy provides crucial extensibility: it allows us to utilize pseudo-sharp images generated by any off-the-shelf deblurring model without requiring retraining of our kernel-based model. To validate this extensibility and demonstrate the impact of pseudo-sharp image quality, we generate reference images using pseudo-sharp images produced by external deblurring models such as NAFNet (Chen et al., 2022), FFTFFormer (Kong et al., 2023), and Restormer (Zamir et al., 2022). Then, use these reference images to train our kernel-free models. As shown in Table 10, these results show that employing higher- quality pseudo-sharp images yields better final deblurring performance. This directly demonstrates that leveraging stronger pseudo-sharp images helps mitigate the potential training-inference inference gap (e.g., training with GT, but evaluating with pscudo-sharp images), leading better final deblurring performance.
Table 10: Effect of different aseudo-shary images RefDeblur- B.
Pseudo-Sharp Model
Final Deblurring Performance
Input Types
Network
PSNR.
LPIPS
PSNR
SSIM
NAFNet
32.66
0.089
33.81
0.941
Blur - Pseudo-Shar
FFTFormnr
32.62
0.089
33.22
0.937
Restormer
32.32
0.108
32.88
0.933
B.3 Effects on model size
Our hybrid model consists of cereel-based and kernel-free models. To obtain a pseudo-sharp image for our kernel-basod model, an additional off-tho-shelf pscudo-sharp model, c.g., pre-trained NAFNet (Chen et al., 2022) is considered. Here, we raise fundamental question: which model size should we increase improve performance? To address this, conduct experiments with various model sizes by scaling the size
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of pseudo-sharp (PS), kernel-based (KB), and kernel-free (KF) models. The results are shown in Table 11. It is reported that the small-scale kernel-based model is sufficient to provide high performance (compare the performance in the 1st-2nd-3rd row results). Furthermore, we observe that the size of the kernel-free model is the most important ingrodient to improve performance. This is witnessed by comparing the 2nd-3rd (KB size increase), 2nd-4th (PS size increase), and 2nd-5th (KF size increase) rows for total MACs and PSNR.
Table 11: Ablation study on model sizes of our method. We make several combinations of pscudo-sharp, kernel-based, and kernel-free models when varying the size of each model. The bolded GMACs indicate the increase compared to the previous row.
MACs (G)
PSNR
SSIM
Pseudo-Sharp
Kernel- Based
Kernel-Free
Total
16.25
1.16
16.30
33.71
32.92
0.931
16.25
3.99
16.30
36.54
33.00
0.931
16.25
14.69
16.30
47.24
33.00
0.931
63.64
3.99
16.30
83.93
33.28
0.933
16.25
3.99
42.37
62.61
33.38
0.938
16.25
3.99
69.25
89.49
33.60
0.940
B.4 Effects on
We investigate which hyperparameter À produces the most suitable reference image. We experiment with {0.001, 0.1, 1.0). Given the reference images varying À from 0.001 to 1.0, the final deblurring results are shown Table 12. Although low À values, such as 0.01 or 0.1, lead to some artifacts, as shown in Fig. (c), they provide a richer, more detailed reference image, thereby resulting in better deblurring performance. On the other hand, when too small, such as 0.001, as shown in Fig. 4 (d), it gives rise to large amount of artifacts, that such high dominance of artifacts in the reference image results in degraded performance despite its high-frequency details contained. Therefore, we choose the hyperparameter À as 0.01 in our experiments.
Table 12: Ablation study on hyperparameter A. We investigate the hyperparameter À since it is related to the quality the reference image, which influences the final performance.
Methods
PSNR
SSIM f
NAFNet-16
31.58
0.912
0.001
32.14
0.919
0.01
32.37
0.921
RefDeblur-16
0.1
32.24
0.918
1.0
32.20
0.916
B.5 Iterative evaluation of the proposed pipeline
We investigate the effect of applying our pipeline iteratively, where the deblurred image generated from the previous iteration is reused as new pseudo-sharp image for the next. This strategy aims to examine whether repeated refinement of our pipeline can lead further improvements in deblurring performance. As shown in Table 13, we observe that performance tends degrade with subsequent iterations. We believe this is because the pseudo-sharp images generated from previous iterations differ in characteristics from those the kernel-free model originally trained with. This lead to 'unseen' input for the next iteration, leading to performance degradation. To confirm this, we perform an additional experiment where the kernel-free model is retrained for the second iteration. In this setup, we use the deblurred images from the first iteration as new pseudo-sharp images for the second. Interestingly, as shown in Table 13, this retrained kernel-free free model shows improved performance. This result supports our assumption that the initial performance drop is caused by the second kernel-free model encountering distt-diisti pseudo-sharp images when reused iteratively without retraining.
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Table 13: Iterative evaluation of the proposed pipeline on RefDeblur-T. Performance measured across
iterations, with and without retraining the kernel-free model using updated pscudo-sharp images.
Evaluation Setting Retraining
PSNR
SSIM
1st Iteration
32.70
0.927
2nd Iteration
32.62
0.924
3rd Iteration
32.57
0.924
2nd Iteration
32.83
0.928
B.6 Spatial vs. Fourier vs. LogFourier
We examine whether the logarithmic fourier space is the most effective input domain in our kernel-basee method. To this end, train our kernel-based models using input samples in spatial, fourier, and logarithmic fourier domains to make reference images which then used to train our kernel-free models. The final deblurring results are reported in Table 14. The results show that the logarithmic fourier space leads to the best deblurring performance among the domains since the logarithmic fourier domain helps enables to find more accurate blur kernels by simplifying the relationship between sharp and blur images (c.g., deconvolution subtraction).
Table 14: Ablation study on input domains in our kernel- based method.
Kernel- Free Model
Architecture
Kernel-Bace Baneed Input Domain
PSNR
SSIM
NAFNet-16
31.58
0.912
Spatial
31.74
0.913
RefDeblur-16
Fourier LogFourier
32.20
0.919
32.37
0.920
C Generalization to real-world blur images
We capture real-world blur images with natural hand motions by Samsung Galaxy Note 20 Ultra. We use our RefDeblur-S model trained with RealBlur-J (Rim et al., 2020) in order to show the effectiveness of our method under the lowest complexity, compared with the previous methods such Stripformer (Tsai et al., 2022) and NAFNet-64 (Chen et al., 2022). As shown in Fig. 7, the real-world blur images are well- reconstructed by our method. Meanwhile, the compared methods work well on some blur images but some other blur images are not well-recovered. This shows a better generalization of our method compared with other methods.
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(a) Blur Input
(b) NAFNet-64
(c) Stripformer
(d) RefDeblur-S (ours)
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Blur Input
(b) NAFNet-64
(c) Stripformer
(d) RefDeblur-S (ours)
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Blur Input
(b) NAFNet-64
(c) Stripformer
(d) RefDeblur-S (ours)
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(a) Blur Input
(b) NAFNet-64
(c) Stripformer
(d) RefDeblur-S (ours)
Figure 7: Qualitative results on real-world scenarios.
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D Additional qualitative results
(a) Blur Input
(b) MAXIM-3S
(c) RefDeblur-B
(d) Sharp
Figure Additional qualitative results on RealBlur (Rim et al., 2020).
25


Reviews:
[{"review_id": "Sbod2YUmov", "reviewer": "Reviewer_6jES", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The paper itself is generally well organized, with comprehensive introduction to the problem and motivation.\n- The method design is simple and effective. \n- The notation in this paper is clear and easy to follow.\n- The method achieves a good balance between performance and efficiency.\n\nWeakness:\n- The paper should elaborate more about why estimate spatial-variant kernel is less effective than the two-stage pipeline proposed in the paper. For example, the authors should vary the network capacity of spatial-variant kernel estimation and see how the performance increase along with network capacity.\n- The kernel estimated in this paper is not guaranteed to be physically valid. There is no constraint enforce the element of the kernel to be positive and sum equals to one. The authors should discuss and proof that this constraint is unnecessary.\n- The paper requires an external deblur network to generate the reference image, it is unclear whether the computational of the external deblur network is included in the computation cost. In addition, the authors did not compare with different external network to generate the reference. Since the kernel estimation network is trained on ground-truth image and there is a potential training-inference gap, the authors should validate different external models rather than leaving it to future work.\n- All the evaluation metrics in the paper are PSNR and SSIM, more advanced perceptual metric, at least LPIPS is necessary.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper works on the problem of blind motion deblur, where the motion blur kernel is unknown. Instead of either estimate the spatial-variant non-uniform kernel precisely or skip the kernel estimation and only predict the deblurred image in a single forward pass, the contribution of this paper is a two-stage strategy. First, a reference image deblurred from off-the-shelf networks and the blurred input images are transformed into frequency domain and a uniform kernel is estimated from the frequency image via a kernel estimation network. Then the authors deblur the blurred input image using this estimated kernel and then predict a residual image which is added to the kernel deblurred image as the final clean image. To validate the contribution of this two-stage strategy, the authors conduct experiments on various dataset and demonstrate improvement on both performance and efficiency.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- First, Please refer to the weakness.\n- Second, I would suggest the author to run the proposed pipeline iteratively, where the reference image is the output from the last iteration and see how the performance improves or diverges.", "broader_impact_concerns": "This paper is about motion deblur which is a low-level image processing problem, therefore these is no concern about ethical issues.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "QNFFEKe3wc", "reviewer": "Reviewer_2gAJ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "There are several strengths:\n\n1. The paper is well writen. The motivation and methodology of the paper is clear. \n\n\nThere are several concerns:\n\n1. The convincement of the experimental results is expected to be further verfied. Specifically, a difference exists in the reported performance of NAFNet on the GoPro dataset. The authors report PSNR values of 33.02dB and 33.04dB in Table 4, which are notably lower than the officially reported result of 33.69dB (PSNR of proposed method is 33.60). Considering NAFNet is one of the most important baselines in the paper, authors are highly expected to clarify the reason for this difference.\n2. The ablation studies are not convincing. It is recommended that the ablation studies be designed to progressively add blocks, starting from the results obtained solely from the Pseudo-Sharp Image processing. It would more clearly demonstrate the incremental improvement provided by the subsequent components building upon the Pseudo-Sharp Image. Furthermore, conducting ablation studies on the GoPro dataset is encouraged to enhance the overall convincingness of the analysis\n3. It may not be appropriate to compare the scalability of the proposed method at large scales, as the method does not introduces extra new information and this step-by-step strategy are usually learned implicitly by large-scale models. However, for lightweight models, this step-by-step or multi-stage methods demonstrate better performance, likely due to the introduction of prior knowledge, and it is therefore requested that the authors demonstrate the performance of the lightweight model on datasets other than RealBlur, particularly in comparison to other lightweight methods. \n4. Several baselines are further expected to be clarified. I think this paradigm of the paper is pretty similar to multi-stage. The authors are encouraged to further discuss the differences from prior arts, such as MPRNet [CVPR 2021] to clearly highlight the novelty and differences of their proposed framework.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this paper, authors propose hybrid framework that decomposes a non-uniform deblurring task into two simpler tasks, i.e., a uniform kernel estimation and error prediction, which are handled by kernel-based and kernel-free methods, respectively. To be specific, the kernel-based sub-module aim to generate a reference image with realistic texture details, and kernel-free sub-module concentrate on refining the reference image by correcting residual errors with preserving texture details. Besides, logarithmic fourier space and regime are introduced into the methods. In the experimental part, authors claim that their proposed method achieves remarkable performance on several datasets, which verifies the effectiveness.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "There are several recommendations of changes here:\n\n1. Clarify the significant discrepancy in the reported PSNR results for NAFNet on the GoPro dataset (33.02dB/33.04dB in Table 4) compared to the officially published result (33.69dB), explaining the reason for this difference. (Critical and very very improtant)\n2. Redesign the ablation studies by progressively adding blocks, starting from the pseudo-sharp image, to more clearly demonstrate the incremental improvement provided by subsequent components. And conducting above ablation studies on the GoPro dataset to further enhance the overall convincingness of the analysis (Recommended)\n3. Demonstrate the performance of the lightweight model on datasets expect RealBlur, particularly in comparison to other lightweight methods. (Recommended)\n4. Clearly discuss and clarify the differences between the proposed framework and existing multi-stage prior arts, such as MPRNet, to highlight the novelty and differences of the proposed method. (Recommended)", "broader_impact_concerns": "There are no further broader impact concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "a1TpUK5BMR", "reviewer": "Reviewer_tHgJ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. The proposed combination of kernel-based and kernel-free methods for motion deblurring is interesting.\n2. The idea of self-generated reference image also sounds interesting.\n\nWeaknesses:\n1. Why do the authors use different networks for the kernel-based and kernel-free models? NAFNet has a similar but stronger architecture than U-Net. Why don't you use it for all stages?\n2. In comparison, if the authors' goal is to recover a sharper, more realistic result based on the reference image, it might be good to add the LPIPS metric for the main tables. And I would also suggest comparing with at least one diffusion-based approach.\n3. Can you remove the individual learning of kernel estimation by training the whole framework end-to-end with a single final reconstruction loss?\n4. In the first paragraph of the introduction, motion blur also occurs due to high-speed movement.\n5. For both Figure 1 and Figure 2, it would be nice to have larger, multiple examples to illustrate the effectiveness of the proposed method. In addition, please explain what $\\lambda$ is in the caption.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes to combine kernel estimation and kernel-free method as a hybrid framework for blind motion deblurring. More specifically, the kernel estimation is used for generating a reference image and kernel-free method refines it by correcting residual errors. The proposed method is evaluated on several datasets to demonstrate its performance.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please see **Weaknesses**.", "broader_impact_concerns": "No concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)