Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising
Authors: Yuhui Quan
Tianxiang Zheng
Hui Ji
Venue: NeurIPS 2024 poster (2024)
Keywords: self-supervision
image denoising
low-level vision
transformer
Primary area: machine_vision
TL;DR: Self-supervised blind-spot transformer for real-world image denoising

Abstract: Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the transformer’s capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel–a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a half-plane grid for self-attention, creating a sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impacts
from the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-image-free methods. This combination of blind-spot and transformer techniques provides a natural synergy for tackling real-world image denoising challenges.

Pseudo-Siamese Blind-Spot Transformers for Self-Supervised Real-World Denoising
Yuhui Quan", Tianxiang Zheng" School of Computer Science and Engineering South China University of Technology
Hui Ji Department of Mathematics National University of Singapore
Abstract
Real-world image denoising remains a challenge task. This paper studies self- supervised image denoising. requiring only noisy images captured single shot. We revamping the blind-spot technique by leveraging the transformer's 's capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel-a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a half- plane grid for self-attention, creating sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impacts from the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-image free methods. This combination of blind-spot and transformer techniques provides natural synergy for tackling real-world image denoising challenges.
Introduction
Images taken with digital cameras inevitably acquire noise from various sources. Image denoising aims to recover a clean (noise-free) image from its noisy counterpart, serving as an important tech- nique many low-level vision tasks. Recently, deep learning has prominently driven advancements in image denoising; e.g.,
& 9 TO I1] Early works typically take supervised learning approach to train a neural network (NN) on dataset with paired noisy and clean images, where noisy images synthesized by corrupting clean images with Additive White Gaussian Noise (AWGN). As observed in 6 12). a denoising NN trained on AWGN fails to generalize well on real-world noisy images due to the statistical distribution gap between AWGN and real-world noise.
To address this issue in supervised learning, several studies have concentrated on constructing datasets with paired real-world noisy and clean images, such as the Smartphone Image Denoising Dataset (SIDD) 13 and the Darmstadt Noise Dataset (DND) 14] However, creating these datasets is labor-intensive, involving multiple acquisitions of images of the same scene, requiring rigorous image alignment, and is inapplicable dynamic scenes. Additionally, the statistical properties of real noise vary for different camera systems and settings. These limitations restrict the wider application of supervised- -iriised--aannnn--ee based solutions real-world image denoising.
To address the difficulties of creating paired noisy-clean image datasets that capture the noise characteristics of the targeted testing data, some approaches train denoising NNs using unpaired noisy and clean images [5] 16) 17 [). Other studies [18] 19 20 use multiple noisy images of the same scene as training data. While these methods relax data collection requirements, limitations remain. The former still requires clean images, which is challenging to collect, especially in scientific/medical imaging. The latter involves rigorous image alignment, making unsuitable for dynamic scenes.
"maill: csyhquan @ scut.edu.c.. (Y. Quan); cszhengtx @ scu...ccu.du (T. Zheng); matjh www..uuu.uu.....mmmmme (H.
Ji).
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Self-supervised denoising methods: In recent years, there has been increasing interest in self- supervised denoising methods, where network training requires only a set of noisy images captured in a single shot. Among these methods, Blind-Spot Networks (BSNs) and their variations are The concept of BSNs has played tasks. In principle, through specific architectural design, a BSN estimates each output pixel from the surrounding noisy pixels, excluding the corresponding one. This design prevents the network from converging an identity mapping when trained to minimize the distance between the output of the NN and the input noisy image, no clean image is available. Note that pixel-wise noise independence is critical for BSN to work effectively.
particularly popular; see, e.g. 21 22 23 25 26
29 30].
significant role in self-supervised learning for various denoising-reland
Recently, transformers, NNs utilizing self-attention (SA) for sequence modeling, have shown great performance many applications, including image denoising; see, for example, 31
32 K
Compared to Convolutional Neural Networks (CNNs), which use multiple convolution layers to extend the receptive field and connect distant pixels, transformers directly model interactions between distant pixels via attention, capturing long-range dependencies more effectively.
The effectiveness of transformers or SA in connecting distant pixels is particularly attractive for self-supervised denoising, especially for Blind-Spot Networks (BSNs). The ability of transformers to exploit long-range dependencies aligns well with the need for pixel-wing BSNs to achieve optimal performance, as noise on distant pixels is more likely to be independent than on neighboring pixels. While the integration of transformers with BSNs is very promising, has not been well studied in self-supervised denoising.
noise independence in
36 integrate SA and BSN for self-supervised denoising. LG-BSN (28) used channel SA [34] with dilated convolutions for blind-spot learning but did not utilize spatial SA. SS-BSN 29 integrated lightweight spatial SA module into BSN with dilated convolutions but lacked rigorous justification of blind-spot constraints. SwinIA [36] applied masking the Swin Transformer [3] attention matrix, targeting only AWGN, As result, these transformer-
Currently, there are few studies 28
29
based methods do not outperform recent CNN- approaches 330 in denoising real-world images. remains an open question how to fully leverage the effectiveness of long-rann aun-range dependence in transformers for better performance in self-supervised real-world denoising.
Main idea of approach: The direct combination of SA and BSD, done in 29 may compromise the blind-spot property (28]. Another approach is to mask out input pixels, as in 21 22). However, pixel masking reduce the integrity and the accuracy of long-range feature interactions within transformer blocks. It also limits the number of available pixels for loss calculation, thereby leads sub-optimal performance. We address these issues with SelfFormer, self-supervised transformer model featuring a built-in blind-spot structure that avoids input pixel masking.
Our SelfFormer is built on directional self-attention (DSA) mechanism, inspired by directional convolution (23. Unlike plain self-attention, where the attention window spreads out in all directions (see Figurd I a)), DSA's attention window is constrained to half-plan excluding the token itself (see Figure Tb)).. This unidirectional flow ensures that information broadcasted by a token does not return to even after multiple DSA applications, thus creating a blind-spot mechanism.
SelfFormer has four branches, each using DSA in one direction: left, right, up, and down. Combining outputs from all branches allows attention to extend arbitrarily far in every direction without including the center pixel. To improve computational efficiency, we use a gridding scheme on the directional attention window. In addition, we introduce channel attention (CA) mechanism with blind-sper spot properties to capture channel interdependence, complementing the spatial similarity captured by SA.
Blind Zone
Current Tokon
Wircow
(a) Plain SA
(b) Proposed DSA
Figure 1: Illustration of basic idea of our approach.
2

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To further enhance the effectiveness of DSA caused by its restricted attention locations, we introduce a Speudo-Siamese architecture for SelfFormer. One sub-NN, SelfFormer-D, employs DSA with four branches, while the other, SelfFormer-FF uses full-grid SA with only one branch. SelfFormer-D's four branches share weights and process four rotated inputs, allowing identical learnable weights for DSA and full-grid SA in the pseudo-Siamese learning. Despite different structures, this setup ensures both sub-networks have consistent weights. SelfFormer-D and SelfFormer-F, with their different inductive biases, provide mutual regularization through joint learning. Due to its better capability of exploiting long-range dependencies with unconstrained attention windows, SelfFormer-I is used for inference.
Contributions:
The main contributions of this paper are summarized as follows:
SelfFormer, an efficient self-supervised transformer, is introduced for real-world image denoising, integrating the blind-spot mechanism with transformers.
DSA, specific type of SA, is developed to embed the blind-spot mechanism in transformers.
A Sseudo-Siamese architecture is introduced to address the potential negative performance impact of the restricted attention locations caused by the blind-spot mechanism.
The proposed SelfFormer for denoising real-world images is evaluated on two popular datasets, SIDD and DND, and compared with many existing image denoisers. The results showed that SelfFormer outperforms existing self-supervised denoisers and other methods that not using clean images.
Related Work
Supervised denoising:
The study of supervised denoisers focus on designed network architecture.
Most existing methods are based on CNNs; see e.g. 2 4 6 7
9 I0
37
IT|
Recently,
transformers have become a primary choice due to their performance ad advantage B1
32 33 34 335.
Training data another concern. Instead of using AWGN for data synthesis, CBDNet [2] synthesized heteroscedastic Gaussian noise and processed it through Image Signal Processor. Zhou ef al. [38
trained noise estimator and denoiser with mixed AWGN and random-vale valued impulse noise and then utilized pixel-shufile down-sampling to adapt the trained model to real noises. For better generalization and evaluation, two widely-used real-world noisy datasets were constructed in [13 [4]. respectively. However, large-scale paired data collection remains challenge.
The related approaches can be categorized into learning on unpaired image data, and learning on paired noisy image data. The former leverages unpaired noisy and clean images, using either generative adversarial networks [15] [6] or flow-based methods [17 [20). The latter use multiple noisy images of the same scene to train a denoiser [8 19 20]. Note that such multi-capture of noisy images still requires image alignment and is not applicable to dynamic scenes.
Weakly-supervised denoising:
Self-supervised denoising: Self-supervised denoisers trained using only set of noisy images captured in single shot. BSN is one popular self-supervised denoiser. Masking-bast based BSNs (c.g.. Noise2Void [2] and Noise2Self 22 address overfitting caused by the absence of clean images in the loss function by masking a portion of input pixels and predicting them using the remaining pixels. Noise2Same (24 introduces additional refffreconstructionn loss to utilize center pixels' information. Blind2Unblind 25 trains a masker to better preserve valuable pixels in the masked input. SASL [39]
gives separate treatment flat and textured regions in masking
based based self-supervision.
large percentage of input pixels are not used in the loss function when they are masked out, leading to sub-optimal performance 23). To address this, some works design specific network architectures to ensure the receptive-fin ffeel center is not seen by the corresponding pixel during prediction. Laine et al. (23] occludes half of the receptive fields of CNN in four different directions. D-BSN (26) applies a center-masked convolution, followed by a series of dilated convolutions with specific step sizes. MM-BSN [40] uses multiple convolutional kernels masked in different shapes.
The less correlated the noise of related pixels is, the better the denoiser performs. However, in real images, neighboring pixels' noise is highly correlated. One solution is to relate distant pixels. AP-BSN (27 uses pixel-shuffle downsampling with high strides training, and low strides testing. PUCA 30 leverages saich-unshuffll/shfffte to expand receptive fields for relating distant pixels. LG-BPN 28 uses channel SA with dilated convolution for relating distant pixels and densely-
sampled pwtch-macceeee. convolution to recover local structures. SS-BSN 29 combines grid SA 4]
3

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with a simplified D-BSN [26]. SwinIA 36 masks the diagonal of the attention matrix in a Swin Transformer (3
Besides BSNs, there are also other approaches which are prchitecure-inddeendnnt SURF-baaed methods (e.g. 42) introduce Stein's unbiased estimator 43 to regularize the training. Recorruption- based methods, e.g., Noisier2Noise [44], R2R [45) IDR [46], and Zheng et al. [47]. define the loss using pairs derived from the input noisy image simulate supervised loss. Sampling-based methods (e.g. Neighbor2Neighboor (48) form training pairs using a random neighbor sampler on the noisy image. Score-based methods, e.g., Noise2Score 49 NDASSID [50, and Xie et al. [5]], perform self-supervised training via score matching [52]. disentanglement-baseee methods, e.g., CVF-SID [3 use cyclic loss function to decompose the noisy image into clean and noisy components.
Zero-shot denoising:
This approach performs per-sample self-supervised training. The sparse- 56 57 from the noisy image, and the denoising output is defined as a sparse approximation to the input based on the learned dictionary. The NN-based denoiser, such as DIP [58]. (59 NoisyAsCleanfdN) Noise2Fast [61] and ScoreDVI [62]. also follow this paradigm. However, this approach is computationally costly when applied to large number of images.
coding-based denoisers learn a dictionary [54] 55
3 Methodology
In this section, we give a detailed description of our proposed SelfFormer, which effectively integrates the blind-spot mechanism with transformers for self-supervised real-world image denoising.
3.1 Grid Self-Attention and Directional Self-Attention
For a set of tokens stored as Z = [æ1;-... ; = E IR L. ,SA seeks to derive new token representations by assessing interdependence among every pair of input tokens. Consider SA in multi-heat setting (63] For the hithh of H attention head, all tokens undergo a linear transform resulting in Qu: K h: Vn E RLLD, which represent queries, keys and values respectively:
(Qu, Kn, V)) = (Zw)) Zw ZWN),
(1)
where / WK WY are learnable matrices. Subsequently, attention weights are derived, deter- mining the extent to which each token interacts with its counterparts. This is achieved through the calculation of similarity scores between queries and keys, leading to:
Head, = softmax Q /.KKK//DDD D)V,.
To consolidate results from all attention heads, the output is given by
SA(Z)= = concat concat(Headd, Heady, ... Headg))Wo
(3)
where O is a learnable matrix dedicated to fusing the results of the different attention heads.
SA involves pairwise comparison of tokens, which is costly when directly working on all spatial tokens from a feature tensor. As an acceleration scheme. grid SA [4 grids the tensor of shape (H, W. C) into the shape (G2, HW C C) using fixed G x G uniform grid, resulting in windows with adaptive size H W Then SA is performed on the decomposed grid axis (i.e., G x G),, corresponding G to dilated, global spatial mixing of tokens. See Figure 2 (a) for an illustration. Note that grid SA does not satisfy the blind-spot property.
Our DSA also uses the gridding trick for acceleration; see Figure for the illustration. Instead of using half-plane attention window ()i DSA employs half- half-planc grid to reduce the number of compared tokens for p. Only subset of equally spaced grids on the half-plan is involved, which lowers computational cost. To enhance diversity, adjacent tokens have different attention grids. For example, tokens of different colors correspond to different grids (Figur(Z (b)). Each attention head shifts the grid differently, ensuring each token visits all grids in one pass.
Our DSA can effectively simulate different attention grids by rotating and shifting the input. Despite different definition of attention grid., DSA and grid SA in our approach share the same parameters O wK W.Y W Whg allowing the introduction of the Siamese structure into our SelfFormer.

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Head #1
Head #2
Head #33
Head #44
Token/ Attention Grid
(a) Grid SA
(b) Grid based multi- heat- implementation of DSA
Figure 2: Illustration of grid SA and grid-based DSA. In grid SA, attention grids scattered in all directions, with attention among tokens on same-colored grids. In DSA, attention grids are constrained to a half-plane, varying by token and attention head.
3.2 Architecture of SelfFormer
As illustrated in Figure the proposed SelfFormer has a pseudo Siamese architecture consisting of two sub-NNs: SelfFormer-D D (D for Directional) and SelfFormer-F (F for Full), both aiming at mapping an input noisy image its clean counterpart. The SelfFormer-D consists of four branches, each performing DSA with one specific direction. The SelfFormer-F has only one branch which has the same structure the branches in SeliFormer-D, except that its performs grid SA instead of DSA.
Each branch consists of six attention blocks (ABs) for performing DSA or grid SA, with two x 1 convolutions the beginning and end. The ABs are arranged in U shape, with downsampling in the first half and upsampling in the second half. All branches in SelfFormer-D and SellFormer-F share weights, except for the last 1 convolution. The last outputs from its four branches, resulting in different input channel dimension from SelfFormer-F. Consequently, SelfFormer-D and SelfFormer-F have different structures but share identical learnable weights, except for the last layer, forming Sreudo-Siamese pair.
convolution in SelfFormer-D integrates
The pseudo-Siamese Siamese architecture benefits both training and testing. While SelfFormer-D" blind- spot mechanism aids self-supervised training to avoid overfitting, its restricted attention grid may weaken the utilization effectiveness of long-range dependency. In contrast, SelfFormer-F uses full attention grid, avoiding this issue though losing the function a BSN. Mutual learning between these sub-networks mitigates SelfFormer-D's structural bias, enhancing effectiveness. Additionally, SelfFormer-F utilizes a single path, as opposed to the four-path structure of SelfFormer-D, resulting in significantly faster performance. Due to these advantages, SelfFormer-F is employed for inference.
Downsampling and upsampling:
The pairs of downsampling and upsampling used in SelfFormer can extend the receptive field in all directions. To maintain blind-spot structure, we implement the same idea in [23] which attaches offsets the downsampling layers. For 2 2 average downsampling layer, we restrict the receptive field to extend upwards only by padding the input tensor with one row of zeros top and cropping out the bottom row before operating downsampling.
Each AB sequentially comprises CA, Feed-Forward Network (FFN), DSA module, and another FFN. ABs involve multiple x convolutions and summation operations, which do need specific treatment for blind-spot mechanism, as they neither change the receptive field nor spread information spatially. The CA is implemented as the NAFBlock [T] in SelfFormer-F, and we construct its blind-spor counterpart (called BSCA) for SelfFormer-D.
Attention blocks:
Channel attention w/ and w/o blind-spot: Channel attention re-calibrates global feature responses for primitive inputs by explicitly modeling inter-dependenciess of channels, complementing the function of SA. We NAFBlock [ for channel attention, consisting SimpleGate and Simplified
Channel Attention (SCA). See Figure
3 for details. Given 2 feature tensors, X, and X2, generated by two 3 convolutions on an input feature, SimpleGate outputs X X1 O X2, where C denotes element-wiss multiplication, maintaining the receptive field. The 3 3 convolutions in SelfFormer-D are directional convolution (3 to own the blind-spot property. Then, SCA re-calibrates channels by multiplying each channel of X with a weight scalar, calculated using channel-wise Global Average Pooling (GAP) and x convolution. As GAP squeezes all spatial values into scalar and scale-multiph ocale-muliplicatioon do not spread spatial information, the SCA preserves the blind-spot property.
5

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Denoised
1:47 Canv
AB
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DSSA
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SelfFormer-D
Emetaa
Cuelf
aolor
1:1 Care
AB
AB
C4
Gr1 SA
RFN
A3
AB
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Coro
colorct
colore
Noisy
Denoised
Noisy
SellFormer-F
Brisa
DerCone
(Gone)
1x] Conte
Dir Grid
(G:3))
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Dineclona Concllatio
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Ext Cony
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(Grd)
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Douusample
Mullypiratoo
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(SCA(CA)
DSA SA)
PFN
Figure 3: Architecture of the proposed SelfFormer.
3.3 Loss Function
The overall loss function consists of self-reconstruutionn loss Setf and mutual learning loss m
Listal Leelf + A.mutual,
where is set 1. Let æ denote noisy image. Let Mp. Mr denote the models of SelfFormer-D and SelfFormer-F respectively. The self-reconstruction loss is then defined by
Exel Ex (= 2||1-
(5)
Due to the blind-sport structure of SelfFormer-D, MD trained with this loss will not converge to the trivial identity mapping, but rather an effective denoiser. The mutual loss is defined by
Lomntall
E. ll - ~Mp(2)ll-
This loss enables the regularization effect of the two sub-networks to each other, leading to better generalization performance.
4 Experiments
4.1 Experimental setting
Three widely-used real-world datasets are used for evaluation: SIDD [3]. DND 14 and NIND SIDD is created by photographing a scene many times and using its clean image. The images SIDD are captured by five different smartphone, and they are divided into non-overlapping subsets for training, validation, and test, respectively. As most works do, these samples are cropped into 24542 pairs of patches. the SIDD-Medium subset is chosen as training data, consisting of 320 noisy/clean image pairs. The validation subset, denoted by VaDD-Valiattio. consists of 1280 paired samples for byper-paramen denoted by SIDD-Bent Benchherch consisting of 1280 noisy samples. DND consists of 50 noisy-clean pairs, formed by shooting the same scene twice with different ISO values. The high-ISO images serve as noisy inputs and the corresponding low-ISO images serve as GT images. DND used only for testing for performance evaluation. NIND is real-world dataset consisting of clean-noisy image pairs captured at ISO levels of 3200, 4000, 5000, and 6400, with 22, 14, 13, and 79 pairs, respectively. For evaluation, select ISO 3200 and ISO 5000, following the provided training/test split. Note
Datasets:
the GT
tuning and ablation study. The subset for test is

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that as the GTss of uploaded the official websites of these two datasets for calculating the peak signal-to-nonl to-nose ratio (PSNR) and the structural similarity (SSIM) index,
and DND are not accessible to users. All denoising results are
Implementation details: Our work is implemented on PyTorch1. 1.00 and CUDA 11.8, which will be released upon paper acceptance. All experiments are conducted on an NVIDIA A6000 GPU. The grid size of SelfFormer-D is set to image size divided by 8, and it doubles for SelfFormer-F, To better address noise correlation, we mask out the X 4 half-plane neighboring locations around the center pixel within the attention window of the DSA of SelfFormer-D during training. This ensures that the pixels used in the attention window are at a distance from the central pixel, thereby reducing the noise correlation as the distance between the pixels increases. SellFormer-D D is optimized using Adam with a learning rate of 0.0001, and that of SellFormer-F is doubled. Other parameters of Adam are set to default. The entire model is trained for 30 epochs for full convergence.
4.2 Performance Evaluation on Real-world Denoising
We include an extensive list of image denoisers for comparison: (a) Two representative non-learning-
based methods: BM3D (65] and WNNM [66]; (b) Three classic supervised denoisers trained on
synthetic noisy data: DrCNN (., CBDNet (22 and Zhou et al. (38]; (c) Five supervised denoisers
trained on real-world noisy images of SIDD-Medium: DnCNN M. VDN [7]. AINDNe((R) ()]
DANet 8 and NAFNet [II: (d) Three unpaired learning methods GCBD 15], C2N+DIDN [I6
and D-BSN+MWCNN [26]: (e) Four zero-shot denoisers: Self2Self [59), NoisyAsClean (60],
Sc0
66 and MASH [67; and (f) Twelve Self-supervised denoisers: Noise2 [11]
2ois (22]. Laine el al. (23) Recorupped2Recoruped (R2R) 45). CVF-SID [53] AP-
BSN 27. L.-BPN 288. MM-BSN 40). SASL. 39] SS-BSN 29 C-BSN 68 and PUCA
30
Table | compares the quantitative results of different methods, where we mark in bold the best results among all methods that do not call any clean images for training, including the non-learning-basee based, zero-shot and self-supervised methods. It can be seen from Table that our SelfFormer achieved the best results on all the benchmark datasets, in terms of both PSNR and SSIM.
R2R and moputtmasking-aae based BSNs, including Noise2 Void, Noise2Self, and Laine et al. 's, rely heavily on the spatial independence of noise. resulting in poor performance on real-world images with locally-correlatee
noise. In contrast, SelfFormer leverages interactions of distant pixels via its transformer architecture during training, leading to significant performance gains. Compared to AP-BSN, MM-BSN, SASI,, and PUCA, SelfFormer's 's superior performance comes from utilizing the transformer's ' long-range perception capability for relating distant pixels.
Regarding SA-based BSNs, including LG-BSN and SS-BSN, our SelfFormer performs noticeably better than LG-BSN, Although SS-BSN matches SelfFormer in SSIM on SIDD, its PSNR performance on DND is noticeably worse. These results confirm the higher effectiveness of our SSA module compared to the dilated SA in LG-BSN and the plain grid SA in SS-BSN
Visual comparison: Refer to Figure ffrr a visualization of denoising results from top BSN-based methods. We selecte the images with richly textured regions from SIDD-benchmark and DND for comparison. Laine et al. failed to break spatial noise correlation, resulting in undesirable denoising. While achieving adequate global denoising. AP-BSN and LG-BPN generate artifacts, and PUCA smooths out some details. We successfully recovers the detailed texture of the clean image by proposed DSA. Unlike HIDD-Benchmark, and DND, the SIDD Volidaiitt provides GT images and we choose some images of it for evaluation. See Figure 5 for a visual comparison of these images relies on global information. Both AP-BSN and LG-BPN fail to separate spatial noise from image details, leading to deficient denoising. In contrast, SelfFormer-F Selfforner-Fefeectively eliminate spatial correlation noise while preventing the creation of erroncous flat regions.
Computational complexity: Table 2 the computational complexity of transformer-based self-supervised denoisers and top CNN-based denoiser, in terms of model size (number of parameters) and inference time for a 256 x 256 image. SelfFormer has a smaller size than the second-best. performer, PUCA, and is much faster than munsformer-basee methods: LG-BPN and SS-BSN.
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Table 1: PSNR(dBY/SSIM results SIDD benchmark, DND benchmark and NIND dataset. We report the official results from the benchmarks' websites whenever possible. The results of Zhou er al.are quoted from [27]. The results of Noise2 and Noise2Self quoted from (45). The results of DrCNN and Laine et al. are obtained by code re-running. Bold denotes the best results in clean-image-free methods.
NIND ISO3200
NIND ISO5000
Type
Method
SIDD
DND
DrCNN
23.66/5533
32.43/.790
Supervised (Synthetic pairs)
Requiring clean images
CBDNet
12
33.28/.868
38.05/942
Zhou et al.
38
34.00/8998
38.40/ .455
DnCNN [
35.87/8422
35.74/1903
VDN 7
39.26/955
39.38/9552
Supervised (Real pairs)
AR
38.84/951
39.34/952
DANet X
39.43/.956
39.58/955
NAFNet
40.30/991
GCBD 15
35,58/992
Unpaired
C2N+DIDN 16
35.35/.937
37.28/.244
D-BSN+MWCNN
37.93/937
BM3D 665
25.65/.685
34.51/851
Non-learning
WNNM
25.78/.809
34.67/.855
Selr2Self [59
29.3T/651
NAC 660
36.20/925
Zero-shot
ScoreDVI 62
34.60/.220
Free from clean images
MASH
34.78/9000
Noise2 21
27 27.68/.668
28.421.766
27.04/.588
Noise2Self 22
29.56/800 808
Laine al. 23
30.14/8233
35.13/862
R2R 45
34.78/8898 898
CVF-SID 63
34.71/.917
36.50/9924
AP-BSN 27
36.91/.931
38.09/.937
34.41/.854
33.49/8847
Self-supervised
LG-BPN
28
9336
38.43/9942
33.94/.80
33.33/831
MM-BSN
40
37.37/.966
38.46/9400
SASL 39
37.41/.934
38.18/.938
SS-BST BSN
37.42/.377
38.46/9940
C-BSN
68
37.43/.936
38 18.62/9422
34.33/.855
33.52/839
PUCA
30
37.54/.936
38.83/942
34.24/.854
33.49/880
SelfFormer [Ours]
37.69/937
38.92/.943
34.43/.857
33.55/8847
Table 2: Computational complexity comparison in terms of model size and inference time.
CNN-based Method
Transformer based Method
AP-BSN
MM-BSN
PUCA
LG-BPN
SS-BSN
SelfFormer-F
PSNR(dB)
36.87
37.38
37.51
37.28
37.32
37.63
#Param(M)
3.7
5.7
12.8
4.8
6.4
10.8
Time(ms)
382
539
529
5208
3976
1812

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xrite
Exirite
exrite
S
DAY ERA
ERA
HYY EN
DR
DR
Laine al. 2
AP-BSN 2
LG-BPN 28
PUCA 50
SeltFormer [Ours]
Figure 4: Visual inspection of the results of some samples from and DND.
Noisy
Ground Truth
AP-BSN 2F
LG-BPN 28
SeltFormer [Ours]
Figure 5: Visual comparison on samples from Volidaaaii.

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4.3 Ablation Studies
Following existing works, we conduct the ablation study on SIDD- Validation. We consider four baselines, summarized in T (a) DSA-+Grid SA: The effectiveness of DSA is tested by replacing with grid SA SelfFormer-D and reducing the branch number to one, resulting pair structure similar to SelfFormer-S. Despite some performance gain from mutual learning. this baseline performs significantly worse than the original model. This is due to the arbitrary feature flow, which loses the blind-spot property. (b) w/o CA: We replace all BSCA (CA) modules with DSA (Grid SA) modules. (c) Only SelfFormer-D: We train SelfFormer-D without m and test with Without the Siamese structure and mutual learning, the performance noticeably decreases due to the limitations of the constrained attention grids in SelfFormer and the loss regularization from mutual learning. (d) Only SelfFormer-F: We train SelfFormer-F only using Letr and test with it. Lacking any blind-spot mechanism and regularization from mutual learning, this network performs very poorly, indicating overfitting to noisy image. (c) w/o DSA: All DSA (Grid SA) modules are replaced with the BSCA (CA) modules. Since the blind spot property is preserved, the performance remains better than "Only SelfFormer-F", which lacks this property. (f) w/o WeightShare: In this case, the SelfFormer-DD and SelfFormer-F do not share weight, leading to performance loss. This suggests that the weight sharing not only reduces the the number of parameters but also provides regularization effect.
Siamese
Table 3: PSNR(dBYSSIM results of ablation studies on SIDD- Validatii.
DSA- SA w/ CA
Only SelfFormer-D Only SelfFormer-F F w/o DSA w/o WeightShare
Original
23.96/0.336 37.54/0.881
37.29/0.879
23.66/0.328
37.22/0.880 37.45/0.880
37.63/0.882
Table 4 shows the experimental results on SIDD-Vas Validation, with varying grid sizes and loss function weight A. The results indicate that increasing the grid size can improve performance, as more pixels are utilized in DSA and grid SA. However, setting the loss weight À too small or too large results in noticeable decrease performance.
Table 4: Results with varying grid sizes and loss function weights SIDD-Validation
Grid Size
Loss Weight
Grid size
PSNR(dB)
SSIM
Loss weight
PSNR(dB)
SSIM
8
37.43
0,874
0.01
37.35
0.879
12
37.51
0.879
0.1
37.57
0.881
16
37.63
0.882
1
37.63
0.882
20
37.65
0.882
37.51
0.880
Conclusion
We presented waasformer-basee self-supervised framework for real-world image denoising, optimiz- ing its performance by exploiting distant pixel interactions in transformer to reduce noise correlation. Our key innovation a DSA module using a half-plane grid for SA, creating blind-spot structure. A Siamese architecture with mutual learning addressed the performance impact caused by the restricted attention grid in DSA. Experiments on benchmark datasets show our method outperforms existing self-supervised and dlean-image-- free methods.
While SelfFormer outperforms all related self-supervised denoisers, wansformer-based models gener- ally incur higher computational costs compared to CNN-based models. Additionally, there remains a significant performance gap between SelfFormer and supervised denoisers. In the future, aim improve both the performance and efficiency of wansformer-basee denoiser.
10

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Acknowledgments
Yuhui Quan would like to acknowledge the support from National Natural Science Foundation of China under Grant 62372186, Natural Science Foundation of Guangdong Province under Grants 50111555 and 2023A151500 2841, Fundamental Research Funds for the Central Universities under Grant X2jsD2230220, and National Key Research and Development Program of China under 2024YFE0105400. Hui Ji would like to acknowledge the support from Singapore MOE Academic Research Fund (AcRF) Tier 1 Research Grant A 8000981-000000
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A Appendix
A.1 Broader Impacts
The proposed self--upervised learning method for denoising images has the potential to impact a wide range applications, including surveillance security, scientific research, and digital media restoration. By improving the signal-to-noise ratio of images, our research can facilitate deeper insights and more effective interventions these fields. In surveillance, higher clarity images can enhance public safety by providing more detailed visual information. However, this also raises concerns about privacy and the potential for mass surveillance. In scientific research, improved image quality can lead to better data and more significant discoveries, though there is a risk that overly processed images could misrepresent the original data. digital media restoration, while the technique helps preserve cultural heritage, it also poses the risk of altering historical records. Despite these possible concerns, our goal to contribute to enhancing image clarity in critical arcas such as public safety, scientific research, and cultural preservation. We emphasize the responsible application and continuous improvement of this technology to mitigate potential risks and maximize its positive impact.
A.2 Additional Results
Table A1: Quantitative results of self-supervised methods on SIDD-Validation.
Metric
N2V
R2R
CVF-SID
AP-BSN
LG-BPN
SASI.
PUCA
C-BSN
SelfFormer [Ours]
PSNR(dB)
27.06
35.04
34.15
36.73
37.31
37.39
37.49
37.51 0.885
37.63 0,882
SSIM
0.651
0.844
0.871
0.878
0.884
0.875
0,880
Table A2:
Computational complexity comparison of different methods in terms of training time, memory usage in training, and memory usage in inference.
CNN-based Methods MM-BSN
Transformer basedd Methods
AP-BSN
PUCA
IG-BPN
SS-BSN
SelfFormer [Ours]
Training Time (h)
5,086
11.291
10.227
6.313
49,483
56.266
Training Mem. (G)
0.574
1.349
2.549
1.884
0.873
9.161
Inference Mem. (G)
0.664
1.310
1.200
12.072
3.492
2.806
For further analysis, we conducted an additional quantitative comparison on the SIDD- Validation dataset, as shown in TA] Moreover, we provide a detailed complexity comparison in Table demonstrating that our method's ss memory usage during inference lower than that of the other two related transformer-basee methods, particularly when compared to LG-BPN. However, due to the inclusion of SelfFormer-D. which incorporates multiple paths for DSA, the memory usage of method during training remains relatively high. This confirms one of our key motivations for introducing the pscudo-Siamer -iadeee architecture, namely to significantly reduce memory consumption from the training phase to inference. For additional visual comparisons DND, SIDD Validation and WODD-Benchmark, please refer to Fig A1 and A3
16

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Laine et al.
23
AP- 27
LG-BPN 28
PUCA 50
SelfFormer [Ours]
Figure AI: More qualitative comparison on samples from DND.
Noisy
Ground Truth
AP-BSN 27
LG-BPN 28
SelfFormer [Ours]
Figure A2: More qualitative comparison on samples from WODD-Valddttio..
17

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O
DRY ERASI
DRY ERA
O
DRY ERI
PU
DRY ERACI
O
DRY ERASI
C
P SE
CLASE
ASE
OA
E
ORY
Laine al. 28
AP-BSN 27
LG-BPN
28
PUCA
BO
SelfFormer [Ours]
Figure A3: More qualitative comparison on samples from SIDD-Benchmark.
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Reviews:
[{"review_id": "w1CR3ZkCFK", "reviewer": "Reviewer_JdHm", "summary": "This paper presents a novel approach for real-world image denoising using self-supervised learning. The method leverages the transformer's ability for long-range pixel interactions, combined with a sophisticated blind-spot structure through grid self-attention and directional self-attention (DSA) modules. Additionally, it employs a Siamese architecture with mutual learning to enhance performance.", "questions": "One major assumption of Blind-spot networks is that noise across pixels is independent. However, several works, such as [R2], have shown that spatial noise in real-world images is often correlated. This correlation needs to be addressed before applying BSN. How did your BSN approach handle this noise correlation, as this was not detailed in the paper?\n\n[R2] Lee, Wooseok, Sanghyun Son, and Kyoung Mu Lee. \"Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022", "limitations": "Yes", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "The paper is well-written and very easy to understand.\n\nThe paper builds upon the approach used in Laine et al.'s work [R1] by replacing CNN layers with attention layers to better capture long-range dependencies, demonstrating an innovative application of transformer architecture in the context of image denoising.\n\nThe experiments convincingly show that the proposed method outperforms existing self-supervised and clean-image-free denoising techniques, providing strong empirical evidence for the efficacy of the approach.\n\nThe approach demonstrates better improvement in terms of time complexity compared to other transformer-based methods, highlighting its practical advantages in computational efficiency.\n\n[R1] Laine, Samuli, et al. \"High-quality self-supervised deep image denoising.\" Advances in Neural Information Processing Systems 32 (2019).", "weaknesses": "The evaluation is limited to SIDD and DND datasets only. More diverse datasets could further validate the method’s robustness.\n\nWhile some ablation studies are provided, further analysis could help understand the contribution of each component in more detail such as the choice of hyperparameters, window size and loss functions.\n\nAlthough the paper claims improvements in time complexity over other transformer-based methods, the computational requirements for training and inference are still likely higher compared to traditional CNN-based approaches, which could limit practical applicability in resource-constrained environments", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your appreciation and valuable feedback. Please find our response and answers below.\n\n**[W1]** *The evaluation is limited to SIDD and DND datasets only. More diverse datasets could further validate the method’s robustness.*\n\nThe SIDD and DND are widely recognized datasets for benchmarking real-world image denoising, with readily available results and trained models of a large number of denoising methods. Therefore, many existing self-supervised denoisers including the SOTA method PUCA [34] mainly use these two datasets for evaluation. For other datasets, there is a lack of published results from many methods. \n\nWe agree with the reviewer that more diverse datasets could further evaluate the robustness of our method. In response to this comment, we conducted supplementary experiments on the NIND dataset [A1], primarily comparing our method against those with available results or codes. For PUCA, the second-best performer in our main paper, we obtained its results by re-training the model using the authors' code. Please refer to Table S1 below for the comparison of the results from different methods. The results, which will be included in the revision, demonstrate that our method maintains superior performance across different datasets as well.\n\n*[A1] Natural Image Noise Dataset. CVPRW 2019.*\n\n**Table S1: Quantitative PSNR(dB)/SSIM results of self-supervised methods on NIND dataset.**\n\n| Method |      |  NIND ISO3200   |      |  NIND ISO5000   |\n| :----: | ---- | :-------------: | ---- | :-------------: |\n|  N2V   |      |   28.42/0.766   |      |   27.04/0.658   |\n| AP-BSN |      |   34.41/0.854   |      | 33.49/**0.847** |\n| LG-BPN |      |   33.94/0.840   |      |   33.33/0.831   |\n| C-BSN  |      |   34.33/0.855   |      |   33.52/0.839   |\n|  PUCA  |      |   34.24/0.854   |      |   33.49/0.840   |\n|  Ours  |      | **34.43/0.857** |      | **33.55/0.847** |\n\n---\n\n**[W2]** *While some ablation studies are provided, further analysis could help understand the contribution of each component in more detail such as the choice of hyperparameters, window size and loss functions.*\n\nThank you for the suggestion. In Table S2 below, we supplement the experimental results using varied values of window (grid) size and loss function weight  $\\lambda$. We can see that increasing the grid size may lead to better performance, as more pixels could be utilized in DSA and grid SA. When the loss weight  $\\lambda$  is set to too small or to large, the performance will decrease noticeably.\n\n**Table S2: Results of ablation studies using different grid sizes and loss function weights on SIDD-Validation.**\n\n| Grid size | PSNR(dB)  | SSIM  | \\|   | Loss weight | PSNR(dB)  | SSIM  |\n| :-------: | :---: | :---: | ---- | :---------: | :---: | :---: |\n|     8     | 37.43 | 0.874 | \\|   |    0.01     | 37.35 | 0.879 |\n|    12     | 37.51 | 0.879 | \\|   |     0.1     | 37.57 | 0.881 |\n|    16     | 37.63 | 0.882 | \\|   |      1      | 37.63 | 0.882 |\n|    20     | 37.65 | 0.882 | \\|   |     10      | 37.51 | 0.880 |\n\n---\n\n**[W3]** *Although the paper claims improvements in time complexity over other transformer-based methods, the computational requirements for training and inference are still likely higher compared to traditional CNN-based approaches, which could limit practical applicability in resource-constrained environments.*\n\nWe agree that transformer-based models are generally more computationally expensive than CNN-based models. However, the impressive performance potential of transformer-based methods makes their study worthwhile. In our experiments, the proposed transformer-based method is faster than other transformer-based self-supervised learning methods. The current weakness of transformer-based methods in computational efficiency is an important research topic, and there is an active and ongoing effort to improve this aspect.\n\n---\n\n**[Q1]** *One critical assumption of blind-spot networks is that noise across pixels is independent ... How did your BSN approach handle this noise correlation, as this was not detailed in the paper?*\n\nThe noise correlation is addressed by masking out the 4$\\times$4 half-plane neighboring locations around the center pixel in the attention window of the directional self-attention of SelfFormer-D during training. By masking out these neighboring pixels, the pixels utilized in the attention windows maintain a distance from the central pixel, and the noise correlation decreases with the distance between two pixels. We will include this detail in the revision.", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "opI1csx0xF", "reviewer": "Reviewer_5hgz", "summary": "This paper explores self-supervised image denoising using only single-shot noisy images. The authors enhance the blind-spot technique by harnessing the transformer's ability to manage long-range pixel interactions, which is essential for eliminating noise dependencies between pixels. Experimental results verify its effectiveness to some extent.", "questions": "1、Could the authors elaborate further on their novelty and explain the differences from the existing works?\n\n2、Could the authors compare the computational complexity and runtime differences between the proposed method and other comparison methods?\n\n3、Could the authors provide the denoising results in real noisy environments (high ISO, high resolution)?\n\n4、Blind spot networks often suffer from issues such as detail blurring and structural distortion, which also seem to be present in this paper. Could the authors provide a more in-depth discussion on these issues?", "limitations": "The limitations have been discussed in this paper.", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Our method introduces a directional self-attention (DSA) module that employs a half-plane grid for self-attention, forming an effective blind-spot structure, and a Siamese architecture with mutual learning to counteract the limitations of the restricted attention grid in DSA. Experimental results on benchmark datasets show that the proposed approach is effective.", "weaknesses": "1、The novelty of this paper lies in the use of directional self-attention to construct the transformer model, enabling a blind spot mechanism and fully utilizing pixel information. This approach has partially appeared in both PUCA (mentioned in the paper) and LGBPN, which reduces the novelty of this paper.\n\n[1] PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising\n\n[2] Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising\n\n2、The pseudo-Siamese architecture requires further explanation. Why does using a full-type network improve overall utilization efficiency? How does this approach differ from the method used in \"Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising\"?\n\n3、Predictably, the introduction of DSA results in multiple branches, which significantly increases the computational load.\n\n4、Experiments on more datasets are needed. For example, the results on the SIDD validation are missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your appreciation and valuable feedback.\n\n**[W1]** *Regarding novelty  \\& difference from PUCA and LGBPN.*\n\nOur work focuses on studying the blind-spot mechanism in a transformer model, with an emphasis on spatial self-attention. Both PUCA and LGBPN also study effective blind-spot mechanisms, but their focuses are quite different.\n\n1.  PUCA is not a transformer-based method; it does not involve spatial self-attention or channel self-attention. Instead, it uses first-order channel attention and pixel shuffle downsampling to achieve the blind spot property.\n\n2.  LGBPN employs channel self-attention but not spatial self-attention in its transformer model. It achieves the blind spot property via masked convolutions.\n\nIn contrast, our method achieves the blind-spot property via directional self-attention and utilizes a Siamese architecture to address the limitations caused by the use of directional self-attention. These differences highlight the unique aspects and contributions of our approach.\n\n---\n\n**[W2]** *Regarding pseudo-Siamese architecture. Why using a full-type network improves overall utilization efficiency? How it differs from the method used in SASL [43]?*\n\nDue to the use of directional self-attention in our SelfFormer-D for gaining the blind-spot property, each branch can only utilize information from the pixels restricted within a half-plane. The results from different directions are then aggregated for the final output, which is not efficient in utilizing the information of all image pixels of the entire plane. The SelfFormer-F in our pseudo-Siamese framework employs full-direction self-attention to utilize information from all directions, addressing this limitation and thus improving overall utilization efficiency. We will elaborate it more in the revision.\n\nThe main difference between our method and the SASL lies in the interaction of different models. The SASL sequentially trains three CNNs without weight sharing, progressively refining the predictions on flat and textured regions. In contrast, our pseudo-Siamese architecture utilizes simultaneous mutual learning between two transformer models with weight sharing but without identical structures, aiming to address the limitations caused by the use of directional self-attention.\n\n---\n\n**[W3]** *Regarding computational load caused by DSA.*\n\nWhile DSA increases the computational load in training, it does not affect the inference time since only the single-branch SelfFormer-F with full-grid self-attention is used for testing. Additionally, we leverage the gridding strategy to reduce the computational load from DSA during training. Considering the performance gain of our method and the un-impacted inference time, even with some computational overhead in training, our method still holds practical value.\n\n---\n\n**[W4]** *Experiments on more datasets. E.g., the results on SIDD-Validation.*\n\nFollowing existing works such as PUCA \\[34\\], we perform evaluations on SIDD-Benchmark and DND. They are two widely recognized datasets for benchmarking real-world image denoising, with readily available results and trained models of a large number of denoising methods. Following your suggestion, we conducted experiments on SIDD-Validation, with results listed in Table S1 below. See also our response to **Reviewer JdHm** for the results on the NIND dataset.  All these results, which will be included in revision, demonstrate that our method maintains excellent performance across these datasets as well.\n\n**Table S1: Quantitative results of self-supervised methods on SIDD-Validation.**\n\n|  Metric  |  N2V  |  R2R  | CVF-SID | AP-BSN | LG-BPN | SASL  | PUCA  | C-BSN | Ours  |\n| :------: | :---: | :---: | :-----: | :----: | :----: | :---: | :---: | :---: | :---: |\n| PSNR(dB) | 27.06 | 35.04 |  34.15  | 36.73  | 37.31  | 37.39 | 37.49 | 37.51 | **37.63** |\n|   SSIM   | 0.651 | 0.844 |  0.871  | 0.878  | 0.884  | 0.875 | 0.880 | **0.885** | 0.882 |\n\n---\n\n**[Q1]** *Regarding novelty.*\n\nPls see our response to [W1].\n\n---\n\n**[Q2]** *Computational complexity \\& runtime.*\n\nPls refer to Table 2 of main text for the results. Our SelfFormer has a smaller size than the second-best performer, PUCA, and is much faster than other transformer-based self-supervised methods, LG-BPN and SS-BSN.\n\n---\n\n**[Q3]** *Results in real noisy environments (high ISO, high resolution)?*\n\nBoth SIDD and DND are composed of images captured in noisy environments with high ISO and high resolution, as described in their descriptions. However, they only provide cropped patches of sizes 256 $\\times$256  or 512$\\times$512. Thus, we only show results on these patches in our paper. In Fig. 1 of our attached PDF, we merge the patches into a whole image for visualization. Our method produces better visual quality than the compared ones.\n\n---\n\n**[Q4]** *Blind-spot networks (BSNs) often suffer from issues such as detail blurring and structural distortion, which also seem to be present in this paper. A more in-depth discussion on these issues?*\n\nDuring training, BSNs utilize neighboring pixels to estimate the central one for self-supervision, assuming strong correlations among neighboring pixels, which is not accurate for edges. As the central pixel is not used, its estimation introduces correlation with neighboring pixels, leading to detail blurring and structural distortion.\n\nOne approach to mitigate such issues is to exploit additional image pixels distanced from the central pixel, whose true intensities are strongly correlated with the central pixel, but whose noise is weakly correlated. This is a key motivation for studying transformer-based models with blind-spot property. Our approach utilizes transformer blocks and self-attention to exploit long-range dependencies within an image and employs a pseudo-Siamese framework to improve the utilization efficiency of long-range pixels. While we cannot completely resolve the issues, our method provides better results with fewer artifacts.", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "aBBVGZb6fz", "reviewer": "Reviewer_UEMY", "summary": "This paper proposes a transformer-based model for self-supervised denoising. The model employs two pseudo-siamese sub-networks during training, but only one is used for inference. One sub-network uses a grid-based DSA for blind-spot learning, while the other utilizes full grid-based attention to mitigate the bias introduced by the blind-spot network. The grid-based scheme is designed to eliminate noise dependence among related pixels. Experimental results demonstrate superior performance on the SIDD and DnD datasets.", "questions": "My main concern lies in the ablation study, where some results appear to be unreasonable. I appreciate that this work advances self-supervised denoising, and I hope the authors can address my confusion. I will adjust my score based on their response.", "limitations": "None", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "+The challenges of this task lie in three aspects: spatially correlated noise, the blind-spot strategy, and the bias introduced by using a blind-spot network (BSN). While the directional blind-spot scheme and grid-based attention are not novel concepts, the authors have proposed an elegant framework that revitalizes these techniques effectively.\n\n+One significant advantage over previous methods is the reduction of bias introduced by the BSN. The pseudo-siamese scheme is particularly effective, as it addresses the bias problem while simultaneously enhancing inference efficiency.", "weaknesses": "-In Table 3,  'w/o DSA' and 'only selfFormer-F' both lack a blind-spot scheme, but why is there a large gap between them?  \n\n-Why does 'w/o CA' perform so worse? the CA is not the main component to address the three key difficulties mentioned above. although the previous method does not design a module to integrate the channel information,  they can perform better than 'w/o CA'.\n\n-typo: line 281 'selfFormer-S'\n\n-There is still significant room for improvement. Currently, supervised learning with real pairs can easily achieve psnr above 38 on the SIDD dataset.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your appreciation and valuable feedback. Please find our response and answers below.\n\n**[W1]** *In Table 3, 'w/o DSA' and 'only SelfFormer-F' both lack a blind-spot scheme, but why is there a large gap between them?*\n\nThank you for pointing this out. We sincerely apologize for the confusion caused by ‘‘w/o DSA’’ in Section 4.3.\n\nThe description of 'w/o DSA' was inadvertently omitted from Section 4.3. The intended description is that 'w/o DSA' replaces all DSA (Grid SA) modules with the BSCA (CA) moduels. Because the blind spot property is retained, its performance is significantly better than 'only SelfFormer-F,' which lacks this property.\n\nThe confusion arises from the mention of ‘(a) DSA-\\>Grid SA’ in Section 4.3. Its results are not reported in Table 3 because its structure is similar to those of 'only SelfFormer-F, as is its performance: 'DSA-\\>Grid SA' achieves 23.96 dB/0.336 in PSNR/SSIM. During the preparation of the paper, we decided to exclude this result from Table 3 but inadvertently left it in the description.\n\nWe sincerely apologize for any misunderstanding caused by this careless error. We will correct it in the revision.\n\n---\n\n**[W2]** *Why does 'w/o CA' perform so worse? The CA is not the main component to address the three key difficulties mentioned above. Although the previous method does not design a module to integrate the channel information, they can perform better than 'w/o CA'.*\n\nThanks for the comment. We found that the unsatisfactory performance of “w/o CA” results from its shallower structure. The “w/o CA” was constructed by removing all BSCA (CA) modules and broadening the 1$\\times$1 convolutions in the network, which made the network much shallower and reduced its expression capacity. To better analyze the effectiveness of CA, we construct a new baseline by replacing all BSCA (CA) modules with DSA (Grid SA) modules. The PSNR/SSIM result of this baseline versus the original model is 37.54dB/0.881 vs. 37.63dB/0.882 on the SIDD-Validation set. This result shows that using DSA (Grid SA) without BSCA (CA) has already achieved good performance, while BSCA (CA) results in moderate performance gain due to their complementary characteristics to DSA (Grid SA). We will update the baseline and its results in the revision.\n\n---\n\n**[W3]** *There is still significant room for improvement. Currently, supervised learning with real pairs can easily achieve PSNR above 38dB on the SIDD dataset.*\n\nWe acknowledge that the performance of self-supervised learning currently still lags behind supervised learning, primarily due to the absence of GT images for training. However, self-supervised learning is valuable in scenarios where collecting GT images is challenging, and it can also be used together with supervised learning to jointly and fully utilize paired data and GT-less data in real applications. Our work has reduced the performance gap between self-supervised and supervised methods. We believe that trading off some performance for the ability to train without GT images is practical and necessary. Our future work will focus on further closing this gap.", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "sja8Qqfffm", "reviewer": "Reviewer_Yeyk", "summary": "This paper presents a novel transformer architecture for real-world image denoising under a self-supervised framework. Following blind-spot networks, they introduced directional self-attention (DSA) module and Siamese architecture to prevent performance degradation from the masked region.", "questions": "1. Does the mutual loss in Eq. 6 affect both $M_D$ and $M_F$, or the output of $M_D$ is detached?\n\n2. What is the principle of weight sharing of DSA and grid SA? Does it improve the performance?\n\n3. Did this method adopt pixel-shuffle downsampling for real-world denoising, which is a widely used block in recent works.\n.\nMinor comments and typos\n - In Eq. 5, missing ( \n - In line 170, two commas\n - In line 262 SSA --> DSA??\n - Full text of BID is missing. Blind image denoising?\n - Line 279, missing space before (a)", "limitations": "The authors adequately addressed the limitations.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Superior denoising performance over existing methods", "weaknesses": "- Limited novelty, where this method presents a transformer-based approach of [1] and employs Siamese training, which resembles knowledge distillation techniques widely used in recent self-supervised denoising with blind spot networks.\n\n- Computational complexity in training. This method shares the computational weaknesses of [1]. \nBoth [1] and the proposed method require four branches to achieve a blind spot in the center, leading to inefficient training that demands large memory and training time. Table 2 only presents the computational complexity of SelfFormer-F without SelfFormer-D, which is the main factor contributing to inefficiency (I guess SelfFormer-D approximately requires four times the memory and processing time compared to SelfFromer-F). Providing a detailed comparison of computational complexity in terms of training time and memory usage would significantly improve the understanding of the proposed method.\n\n\n.\nReferences\n[1] Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. Advances in Neural Information Processing Systems, 32, 2019.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your valuable comments. Please find our response below.\n\n**[W1]** *Regarding novelty over [27] and knowledge distillation (KD) used in recent self-supervised denoisers with BSNs.*\n\n**Novelty and contribution clarification**\n\n- As acknowledged by **Reviewer JdHm**, our approach \"builds upon the approach used in Laine et al.'s work [27] by replacing CNN layers with attention layers to better capture long-range dependencies, demonstrating an innovative application of transformer architecture in the context of image denoising.\" It is important to note that the blind spot property in transformers cannot be trivially achieved by directly migrating it from CNNs due to the fundamentally different structures of attention mechanisms (transformer) versus convolutions (CNN).\n\n- As highlighted by **Reviewer UEMY**, \"while the directional blind-spot scheme and grid-based attention are not novel concepts, the authors have proposed an elegant framework that revitalizes these techniques effectively\". While these two concepts are not new individually, their integration to achieve the blind spot property in transformers is novel. This integration results in a cohesive system that enhances the utility and performance of these techniques in image denoising.\n\n**Comparison to existing techniques related to KD**\n\nWe have reviewed two published works that utilize KD for self-supervised denoising: extended version of Neighbor2Neighbor [A1] and SASL [43]. These methods distill multiple CNNs (without weight sharing) into a single model to improve performance. In contrast, our method specifically optimizes the pseudo-Siamese framework for transformer models by employing weight sharing and mutual learning. This optimization effectively addresses the bias caused by the proposed directional self-attention mechanism. This distinction shows our method's unique approach to leveraging Siamese training to exploit the potential of transformer-based denoisers.\n\nIn summary, while our method builds on existing concepts and techniques, it introduces significant innovations in integrating these techniques and concepts in ways not previously explored in the literature. This integration addresses the technical challenges of enabling the blind-spot property in transformer models, leading to noticeable performance gains over existing self-supervised denoisers.\n\n*[A1] Neighbor2Neighbor: A Self-Supervised Framework for Deep Image Denoising. TIP 2022.*\n\n---\n\n**[W2]** *Regarding computational complexity in training and Table 2 of main paper.*\n\nTable 2 of main paper reported the computational complexity of SelfFormer-F w/o SelfFormer-D,  because SelfFormer-F is the only model utilized during inference. One of our key motivations for introducing SelfFormer-F was to enhance inference efficiency and reduce testing time. By employing SelfFormer-F exclusively during inference, we achieve significant improvements in computational efficiency, making our method more practical for real-world applications.\n\nRegarding the computational cost of training, Table S1 below provides a comparison of training time and related memory usage, as well as inference time and related memory usage. As the reviewer mentioned, the four-branch design requires more memory, but its training time is similar to that of SS-BSN. Moreover, the inference time and related memory usage of our method are less than that of the other two related transformer-based methods. Specifically, the inference time of our method is approximately 34% and 45% of LG-BPN and SS-BSN, respectively. Considering the performance gain and efficient inference of our method over existing transformer-based models, even though there is some trade-off in training time and related memory usage, our method holds practical value. We will include the above analysis in the revision.\n\n**Table S1: Time \\& memory usage in training \\& inference.**\n\n|   Complexity Metric        | AP-BSN | MM-BSN |  PUCA  | LG-BPN | SS-BSN | Ours |\n| :-----------------: | :----: | :----: | :----: | :----: | :----: | :----------: |\n|  Training Time (h)  | 5.086  | 11.291 | 10.227 | 6.313  | 49.483 |    56.266    |\n|  Training Mem. (G)  | 0.574  | 1.349  | 2.549  | 1.884  | 0.873  |    9.161     |\n| Inference Time (ms) |  382   |  539   |  529   |  5208  |  3976  |     1812     |\n| Inference Mem. (G)  | 0.664  | 1.310  | 1.200  | 12.072 | 3.492  |    2.806     |\n\n---\n\n**[Q1\\]** *Regarding mutual loss.*\n\nThe mutual loss affects both $\\mathcal{M}_D$  and $\\mathcal{M}_F$, as the output of $\\mathcal{M}_D$ is not detached. This means that both models are influenced by the mutual loss during training, allowing them to learn collaboratively to improve their performance.\n\n---\n\n**[Q2]** *Regarding weight sharing of DSA and grid SA.*\n\n\nAll four branches in SelfFormer-D and the single branch in SelfFormer-F have the same structure, allowing for weight sharing between DSA and grid SA. To illustrate the impact, Table S2 compares the results w/ and w/o weight sharing, which will be added in the revision. We can see that weight sharing not only reduce the training parameter number but also contributes to a regularization effect, potentially improving the model's generalization.\n\n**Table S2: Ablation study of weight sharing on SIDD-Validation.**\n\n| Weight Sharing | PSNR(dB) | SSIM  |\n| :------------: | :------: | :---: |\n|      w/o       |  37.45   | 0.880 |\n|       w/       |  37.63   | 0.882 |\n\n---\n\n**[Q3]** *Whether adopt pixel-shuffle downsampling?*\n\nOur approach does not adopt pixel-shuffle downsampling. Instead, to address noise correlation, our implementation masks the 4$ \\times$4 half-plane neighboring locations around the center pixel in the attention window of DSA in SelfFormer-D during training. By masking out these neighboring pixels, the pixels utilized in the attention windows maintain a distance from the central pixel, and the noise correlation decreases with the distance between two pixels. We will clarify it in revision.", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:
We sincerely thank the reviewers for their valuable feedback and insightful suggestions. We appreciate the constructive nature of this review process and are committed to thoroughly addressing each aspect.

Meta-review: All reviewers give positive rating scores and the authors' rebuttal addresses the reviewers' concerns well. Of course, there are some minor comments. For example, as Reviewer JdHm pointed out, the computational demands remain a significant concern compared to CNN-based approaches, particularly in resource-constrained environments, like edge devices. Some other reviewers (e.g, Reviewer Yeyk) also mentioned computational complexity. The authors are encouraged to make it clearer in the revision based on the reviewers' comments.
Decision: Accept (poster)