DIFFSSR: Stereo Image Super-resolution Using Differential Transformer
Authors: Dafeng Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Stereo Image Super-resolution
Differential Transformer
Primary area: applications

Abstract: In the field of computer vision, the task of stereo image super-resolution (StereoSR) has garnered significant attention due to its potential applications in augmented reality, virtual reality, and autonomous driving. Traditional Transformer-based models, while powerful, often suffer from attention noise, leading to suboptimal reconstruction issues in super-resolved images. This paper introduces DIFFSSR, a novel neural network architecture designed to address these challenges. We introduce the Diff Cross Attention Block (DCAB) and the Sliding Stereo Cross-Attention Module (SSCAM) to enhance feature integration and mitigate the impact of attention noise. The DCAB differentiates between relevant and irrelevant context, amplifying attention to important features and canceling out noise. The SSCAM, with its sliding window mechanism and disparity-based attention, adapts to local variations in stereo images, preserving details, and addressing the performance degradation due to misalignment of horizontal epipolar lines in stereo images. Extensive experiments on benchmark datasets demonstrate that DIFFSSR outperforms state-of-the-art methods, including NAFSSR and SwinFIRSSR, in terms of both quantitative metrics and visual quality.

DIFFSSR: Stereo Image Super-resolution Using Differential Transformer
Dafeng Zhang
Samsung R&D Institute China-Beijjng (SRC-B)
dfeng. zhang@samsung com
Abstract
In the field of computer vision, the task of stereo image super-resolution (StereoSR) has garnered significant attention due its potential applications in augmented reality, virtual reality, and autonomous driving. Traditional Transformer-baser models, while powerful, often suffer from attention noise, leading to suboptimal reconstruction issues in super-resolved images. This paper introduces DIFFSSR. novel neural network architecture designed address these challenges. We introduce the Diff' Cross Attention Block (DCAB) and the Sliding Stereo Cross- Attention Module (SSCAM) to enhance feature integration and mitigate the impact of attention noise. The DCAB differentiates between relevant and irrelevant con- text, amplifying attention important features and canceling out noise. The SSCAM, with its sliding window mechanism and disparity- attention, adapts to local variations in stereo images, preserving details, and addressing the per- formance degradation due to misalignment of horizontal epipolar lines in stereo images. Extensive experiments on benchmark datasets demonstrate that DIFF- SSR outperforms onte-oofttheart methods, including NAFSSR and SwinFIRSSR. in terms of both quantitative metrics and visual quality. Code is available httpss/ggttlbbboomm////aagD/DDD
Introduction
The rapid advancements deep learning have led to significant progress in the field of single image super-resolution (SISR) [1,44 where the goal is reconstruct high-quality images from their degraded low-resolution counterparts. The impressive results achieved by deep learning models in this domain have naturally piqued the interest of researchers in leveraging the complementary information present in stereo image pairs to further enhance resolution through deep learning methods [5] 6, 4 This has directed the focus towards the exploration of new fundamental neural network architectures and utilization of the complementary information present in the left and right views for stereo image super-resolution (StereoSR).
Among the various neural network architectures that have emerged, SwinIR 3 stands out as simple yet effective baseline for image super-resolution task. It introduces the Swin Transformer [7] as powerful backbone to capture long-range dependencies and global interactions within images, and achieves -hee--rrtteertt (SOTA) performance in various restoration tasks, including image super- resolution, denoising, and JPEG compression artifact reduction. SwinFIR [4) an extension SwinlR, incorporates Fast Fourier Convolution (FFC) ( further enhance the model's ability capture global information and achieves new SOTA results in image super-resolution benchmarks. In the field of StereoSR, SwinFIRSSR 4 extends SwinFIR's capabilities by fusing left/right viewpoint features using a stereo cross-attention module (SCAM) [6]. This adaptation allows SwinFIRSSR to effectively utilize the complementary information from stereo pairs, resulting sharper and more detailed stereo super-resolved images.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Flcort
KETTI
21.00
21.4
21.0
1410
20:4
DEP
Ok
DEP
KITTI 2015
27.6
AVV
J864
30.00
XXI
3600
8010
SwerT155R
DCP
Figure 1: The comparison results with Swin- FIRSSR. DIFF denotes replacing its Trans- former with the DIFF Transformer.
Figure 2: On the horizontal epipolar line, the left and right views are not strictly aligned. Therefore, the performance of SCAM will be hindered.
The core of the Transformer's success lies in its attention mechanism [ which enables the model to weigh the importance of different tokens in sequence. However, recent studies have highlighted the challenges faced by large language models (LLMs) in accurately retrieving key information from context, limitation that stems from the non-negligible attention scores assigned to irrelevant context 10 IT. often termed as attention noise. Therefore, SwinFIRSSR inevitably contains the flaws of the Transformer. Ye al. [T] introduce the Differential Transformer (DIFF Transformer), novel architecture that amplifies attention to relevant context while effectively canceling out noise. So, can the DIFF Transformer also yield significant performance improvements in visual tasks? We substituted the Transformer architecture in SwinFIRSSR with the DIFF Transformer. However, experimental results revealed that the DIFF Transformer not only failed to enhance performance but also degraded the performance of existing methods, as shown in Figure I Consequently, this paper presents a redesign of the DIFF Transformer, tailored for stereo super-resolution task. This is the inaugural exploration of the DIFF Transformer in visual tasks.
In addition the exploration of the basic network structure, within the domain of StereoSR, several methods also have been developed to leverage the cross-view information present in stereo image pairs. PASSRNet [12] introduced parallax attention mechanism effectively handle stereo images with large disparities. iPASSR (5) further improved upon this by incorporating a bidirectional parallax attention module (biPAM) and an inline occlusion handling scheme. NAFSSR (6) employs a Stereo Cross-Attention Module (SCAM) to integrate cross-view information. SCAM is designed to seamlessly blend the simplicity and effectiveness of NAFNet [3] with the distinctive attributes of stereo super-resolution task. By focusing on cross-view attention. SCAM enhances the model's ability integrate features from both views, improving the overall quality of super-resolved images. While SCAM is effective for cross-view features fusion, it only focuses on features corresponding to the horizontal epipolar line. If image distortion or stereo correction algorithm errors cause local misalignment in stereo images, it will hinder the performance of SCAM, as shown in Figure
In this paper, we propose novel neural network structure for StereoSR tasks based on the DIFF Transformer, named DIFFSSR. We have re-engineered the DIFF Transformer and introduced the Diff' Cross Attention Block (DCAB) to address the inapplicability of the existing DIFF Transformer in visual tasks. Additionally, our DCAB also effectively increases the opportunity for information exchange between the two stereo images, alleviating the issue of insufficient communication between the left and right view information in SwinFIRSSR, which can further enhance the performance of super-resolution. We have also redesigned the Stereo Cross-Attention Module (SCAM) and introduced the Sliding SCAM (SSCAM), which addresses the performance degradation of SCAM due to the misalignment of horizontal epipolar lines in stereo images. Through the aforementioned improvements, our proposed DIFFSSR has achieved state-of-the-an (SOTA) performance across multiple benchmark datasets and significantly outperformed SwinFIRSSR.
The contributions can be summarized as follows:
We introduced novel neural network architecture named DIFFSSR, specifically designed for stereo image super-resolution tasks. This architecture tailored to leverage the comple- mentary information present in stereo image pairs to enhance resolution.

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We proposed the Diff Cross Attention Block (DCAB), module engineered to address the iaapliccablityy of the existing DIFF Transformer in visual tasks. The DCAB is designed to differentiate between relevant and irrelevant context, effectively amplifying attention to i important features and canceling out noise.
We proposed the Sliding Stereo Cross-Attention Module (SSCAM), an innovative redesign of the traditional Stereo Cross-Attention Module (SCAM). The SSCAM addresses the performance degradation due to misalignment of horizontal epipolar lines in stereo images by employing sliding window mechanism.
Through the integration of the DCAB and SSCAM within the DIFFSSR framework, we achieved oftte-of-the-rtt (SOTA) performance across multiple benchmark datasets. This was demonstrated through both quantitative metrics and qualitative visual comparisons, outperforming existing methods and validating the effectiveness of our proposed architecture in enhancing stereo image super-resolution.
2 Related Work
The pursuit of enhancing image resolution has been longstanding endeavor in the field of computer vision. Particularly, Stereo Image Super-Resolution (StereoSR) has garnered significant attention due to its potential applications in augmented reality, virtual reality, and autonomous driving.
2.1 Single Image Super-Resolute (SISR)
SISR has witnessed remarkable progress with the advent of deep learning. Early methods like SRCNN [1] introduced the concept of using convolutional neural networks for super-resolution tasks. Subsequent works, such as VDSR [14] and EDSR [15], improved upon these foundations by employing deeper network architectures and skip connections mitigate gradient degradation issues. RCAN (2) further advanced SISR by incorporating a recursive residual structure and attention mechanisms [6.. which allowed the model capture contextual information effectively. These methods, while effective, primarily operate on convolutional neural network, limiting their ability to encode rich contextual information. Based on the Swin Transformer, SwinIR achieves state-of-the art performance in SISR by leveraging the Transformer's 's ability to capture long-range dependencies and global information effectively. As an extension of SwinIR, SwinFIR 4 incorporates fast Fourier convolution to enhance the model's ability to capture global information, resulting in improved super-resolution performance.
2.2 Stereo Image Super-Resolute (StereoSR)
StereoSR leverages the complementary information from stereo image pairs enhance resolution. Early approaches, such as StereoSR (17), utilized disparity priors to improve the quality of super- resolved images. PASSRNet (12) introduced the Parallax Attention Mechanism Module (PAM) to address the challenge varying disparities in stereo images. iPASSR [5] incorporates Bidirectional Parallax Attention Module (biPAM) and an inline occlusion handling scheme. The biPAM designed to symmetrically process both left-to-right and right-to-left parallax information, leveraging the sym- metry cues in stereo images for enhanced super-resolution. NAFSSR G stands approach that integrates NAFNet's simplicity with the distinctive attributes of StereoSR tasks through the use of stereo cross-attention modules (SCAM). Recently, Transformer-basee architectures have been explored in StereoSR due to their ability to capture long-range dependencies. SIR-Former (18) pioneered the use of transformers in StereoSR, employing a cross-attention module learn epipolar line relationships and transformer- based fusion module for accurate cross- cross-view feature integration. Steformer [9 leveraged self-attention to capture both cross-vies -ross-view and intra-view view information in stereo images, ensuring reliable stereo correspondence and effective cross-view integration. Swin- FIRSSR [4] utilizes the strengths of the Swin Transformer and fast Fourier convolution to achieve SOTA performance in StereoSR. Specifically, it integrates frequency domain knowledge through fast Fourier convolution, enhancing the model's ability to capture global information. However, similar to other Transformer-bast based models, SwinFIRSSR faces challenge such increasing attention noise.
lightweight

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3 Methodology
3.1 Preliminary: Differential Transformer
The Differential Transformer [1]] was originally proposed to mitigate the problem of attention noise, phenomenon where conventional attention mechanisms assign non-negligible weights irrelevant tokens, thereby diluting the representation of key information. To address this limitation, Ye er al. [1] proposed differential attention mechanism that constructs two complementary attention branches: one emphasizing relevant information and the other modeling irrelevant or noisy signals. The contrast between the two branches effectively suppresses shared distractions while amplifying meaningful information.
Given input feature sequence æ E RNNC the sets of query and key projections generated as:
-2) xWQ. [g] zWK. V zWv,
where Wo. WK. Wy E CCC denote the learnable projection matrices. Q2 K2 K2 E R and V E NCC denote the query, key and value. N and C are the number of tokens and channels, respectively. The differential attention formulated as:
VC (QrKk)= (s(()) AA
Q2K4
(M,
)v.
where S(-) denotes the softmax function and À is a learnable scalar controlling the suppressive branch. The first term enhances attention toward relevant features, while the second term cancels noise-induced activations, yielding cleaner and more discriminative feature maps. À is formulated as:
À exp(sum(A1g * Ax1)) - crp(sum(() Agz Axz)) + Ainsts
where Ap1, A42, Àgt Au2 E R are learnable parameters, and Ainit 0.8 in this paper.
This differential attention mechanism enables the model to emphasize meaningful context while reducing redundancy. However, directly applying this mechanism to dense visual data introduces computational efficiency and performance challenges. To overcome these issues, we reformulate and extend the differential attention paradigm for the visual domain, which serves the conceptual foundation of our proposed architecture, DIFFSSR.
The DIFFSSR architecture, depicted in Fig- ure Bia), is composed of multiple Diff Cross Attention Blocks (DCAB) and Sliding Stereo Cross-Attention Module (SSCAM). The DCABs are responsible for differentiating be- tween relevant and irrelevant context, amplify- ing attention to important features and canceling out noise. The SSCAM, on the other hand, with its sliding window mechanism and disparity- based attention, adapts to local variations in stereo images, preserving details, and address- ing the performance degradation due to mis- aligmment of horizontal epipolar lines in stereo images. The detail information of DCAB and SSCAM are described in Section and Sec-
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tion 3.4
3.2 Overall Architecture
MIP
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Masto Matip
X Sottrax
As illustrated in Figure(]aa) the proposed DIFF- SSR architecture consists of three main com- ponents: (1) a shallow feature extraction mod- ule, (2) deep feature extraction module com- posed of stacked Diff Cross-Attention Blocks (DCABs), and (3) reconstruction network.
[llenet wise Sammation
Rearsagg
(i) Diff Cross Attention Lager
Soldiingg Steceo Closs, Attention Module
Figure 3: The overall network architecture of DIFFSSR. W-MDA window-based multi-head differential attention module. SSCAM is sliding stereo cross-attes attention module.

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Given a pair of low-resolution stereo images X", _x I2 xx33 the shallow features are first extracted as:
F H =
where H-(+) denotes a 3 convolutional layer used for low-level feature extraction.
The deep feature extraction network is composed of / DCABs, which progressively refine both intra-view and cross-view features:
F (F')s F = HD(FS),
(5)
where H-(+) represents the stacked DCABs. Finally, the reconstruction module recovers high- resolution images by combining shallow and deep features through residual learning:
2 = HnEC (F) + F SR HREC FB F
where Hasc(-) consists of a convolutional layer followed by pixel-shuffle upsampling 20 This
residual design effectively preserves gighfrequency textures, focusing the network on reconstructing fine details.
3.3 Diff Cross-Attention Block (DCAB)
To extend the Differential Transformer for visual tasks, propose the Diff' Cross-Attention Block (DCAB), which introduces a Window-based Multi-head Differential Attention (W-MDA) and a lightweight stereo feature coupling mechanism. Unlike the original global attention used in text processing. W-MDA operates within local non-overlapping windows, significantly reducing computa- tional complexity while preserving spatial locality. Each DCAB consists of three Diff -ttss-Attnniion Layers (DCAL) followed by a 1 x convolution to enhance translation equivariance. Formally, for the j-th DCAL in the i-th DCAB, the features are updated as:
P = AC (F
FR DC (F. (F:3-));
and the output of the i-th DCAB is obtained as:
Fl (F
F. = cone (F.N):
(8)
where (0 denotes a 1 x convolutional layer.
Each DCAL integrates three key components: differential attention module, sliding stereo cross-attention module (SSCAM), and forwafo network (MLP). To adapt the differential attention for visual data, we make two crucial modifications to the original formulation: a. Instead of using the Rotary Position Embedding (RoPE) [21), we adopt Relative Positional Encoding (RPE) 7. RPE explicitly models the relative spatial offsets between pixels, which better preserves spatial consistency within local windows, especially important for stereo correspondence. This substitution improves spatial awareness without introducing additional learnable parameters. b. The original Differential Transformer employs SwiGLU [22] [3) activation within its feed-forward block. However, replace it with conventional two-layer MLP for two reasons: (1) MLPs require fewer computational resources and are easier to optimize for high-resolution image data; (2) SwiGLU's gating mechanism may impede gradient propagation in dense spatial domains, causing information bottlenecks. Empirically, we find that this replacement leads to better convergence stability and performance. The complete computation process inside one DCAB is summarized as:
F W-MDA(LN + F4,
(9)
FR W-MDA(LN(F")) F Fh = (F FL SscaamY ER sscam SSCAM FRR)
(10)
Fl = (N
(11)
(12)
Plup M LP sc )) FL Becam
(13)
F LLP (F () + FR F
(14)
where W-MDA(). SSCAM(-) and LL())) denote the differential attention operator, Sliding Stereo Cross-Attentio Module (SSCAM) and MLP, respectively. L.((.) is the layer normalization.
Fisff. Fiscam and Fissp denote the outputs of differential attention, SSCAM and MLP, respec- tively. This structure amplifies critical spatial cues, reduces attention noise, and enables effective communication between left and right stereo features.
5

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3,4
Sliding Stereo Cross-Attention Module (SSCAM)
While the DCAB focuses on refining intra-view representations, the Sliding Stereo AttestAttnn Module (SSCAM) is designed to facilitate adaptive cross-view feature fusion, as shown in Figure c(c). Traditional stereo cross-attention [6] relies on strict horizontal epipolar alignment, which can be disrupted by imperfect calibration or local distortion. To mitigate this issue, SSCAM introduces a Aliding-windoog cross-attention mechanism that dynamically attends to locally relevant disparity regions. The SSCAM functional expression can be described as the Equation (12). In detail, given the left and right features F* and F", the fused feature F are obtained by:
Q' =Unfold(We" F²), K = W FR. VR= =V= FR.
(15)
(16)
FL P Attention(Q" K v v²) + FR
(17)
where W WK WV and W are the 1 x 1 point-wise convolution for left to right attention. fnfol(() denotes the Unfold operation used in this paper implement a sliding window. Q4, K",, and V denote the query, key and value for left to right attention. Attention(.) is the Stereo Cross-Attention that can be represented as:
Attention (Q. CettatioonQQRK0 = SS( QKT V VC
(18)
The fused feature F R are obtained by:
-Unfold(WW"" F²),,
(19)
KL W pL. vz= WV pt, FR++ WP* Attention K4, FF.
(20)
(21)
where WOR W W. and WPL are the x 1 point-wise convolution for right to left attention. QR, KL,, V denote the query, key and value for right to left attention. Through this formulation, SS- CAM achieves robust cross-view correspondence and effectively handles stereo pairs with misaligned epipolar lines, therehy improving reconstruction fidelity.
4 Experiments
4.1 Implementation Details
Dataset. The training dataset for our proposed model consists of a combination of images from the Flickr1024 dataset [12] and the Middlebury dataset [24]. Specifically, we utilize 800 stereo image pairs from Flickr1024 and 60 pairs from Middlebury. Then, low-resolution (LR) images are created by applying bicubic downsampling to the HR images with scaling factors of x and x4. The resulting LR images cropped into 32x96 patches with a stride of 16, and their HR counterparts undergo corresponding cropping. For testing, we employ popular benchmark comprising 20 pairs of images from the KITTI 2012 dataset (25), 20 pairs of images from the KITTI 2015 dataset (26), 112 pairs of images from the Flickr1 dataset [2] and 5 pairs of images from the Middlebury dataset [24]
Model Setting. Our DIFFSSR architecture is primarily composed of a shallow feature extraction network, a deep feature extraction network, and reconstruction module. The deep feature extraction network is the core component of our model, designed to recover the missing texture details in low-resolution images. It is primarily constructed from 13 Diff Cross Attention Blocks (DCAB), each which consists of Diff Cross Attention Layers followed by a convolutional operation, with channel number of 180.
Training Settings. The training process for DIFFSSR is conducted over 500,000 iterations with a batch size of 8. We initialize the learning rate at 2e-4 and employ cosine annealing strategy [27 to gradually decrease the learning rate le-7. Data augmentation techniques, including random hori- zontal and vertical flips and channel shuffle 4 are applied to enhance dataset diversity. Additionally,

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Table 1: Quantitative results achieved by different methods on the KITTI 2012 [25] KITTI 2015 (26..
Middlebury [24], and Flickr [2) datasets on the RGB space for stereo image SR. #P represents the number of parameters of the networks. Here, PSNR /SNR/SSIM values achieved on both the left images (i.e., Left) and a pair of stereo images (i.e., (Left + Right) /2) are reported.
Mathod
Scale #P
Ligt
(Lef + Right /2
KITTI 2012
KITTI 2015
MicTlebury
KITTI 2012
KITTI 2015
Middlebury
1024
EDSR 15
30.8300 30.33/099999
29.94/0.9251
34.84/0.9489
30.6//0 92238
30.73/0,9335
34,95/0.9492
28.66/00900
RDN 29
x22
22.0M
30.1/009117
29.91 90.924
3485/009488
30944009227
90,0000,9300
34.94/0.9491
64/00.004
RCANE
x/2
15.3M
30.88/0 92.222
29.97 (0.921
34.80/0.00 0.9442
31.02/0.9232
90.77/0.9566
34.90/0.94400
28 63.63/09002
StrooSR 17
X2
1.0M
29.42/0.500
28.53/0.90058
33.15/0.9343
29.51/0.9073
29.5 29.33/0.9165
33.23/0.93448
25 96/0 0.899
PASSR
12
X2
1.37M
30.68/099159
29.99999919
34.13/0.9421
30.81 /0.9100
31.60,0.9500
34.23/0.9422
28 26.388009003
IMSSRoc
6.84M
30.90/-
29-
30.92/-
30,66,-
54.67/
IMASR
1.37M
30,97/0.9211
30,01 (0.924
21.1100.9444
31.11/092100
30.81 (1/00
31.51,00.445
28.6000.9007
SSRDE-FNel
X2
7.10M
31 9277
30. 13,4
35.02/00.95000
31.23/00.2544
90/00.933
35.09/0.95111
28. 85/00.1133
NAPSSR-TB
X2
0.45M
91.12
50. 19,9990
/4.99/999955
51 $1.26,00.9255 92.544
30,99/00.955
35.01 /0.9495
2X 94.9499/122
NAPSSR-S G
X22
1.54M
51.23 (0.926
50.28,00.220
35.23 15.23/0.9515
51.38800.9260
$1.08, /0.30 (0.9567
35. 00.9010
29, 10.199099160
NAFSSR-BI 6
X2
6.77M
31 31.45/099254
90.42.0.9222
35.62/0.9545
31.55/00.9283
31.22.0.9350
35. 1666600,955 10,544
29 09.54.0.900 04000.924
NAPSSR-1
23.79M
11266
91.46/002299
35.83/0.9955 95550
31.25/009356
15.88/0.9557
68,00 10.921
SSRFFRRSSR
x2
23.9MM
(0.929
80,66/00321
06.48/0.9601
31,79/009931
91,45,0.9433
36.52/0.9598
MSSFNe((33 53]
x2
180M
31.37/00.262
80.57/08287
35. 07.9555
31.53/0.9292
31,16/099344
35.8 35.82/0.9553
29, 45 45.022212
DIFPSSR-T (Ours)
X2
1.54M
51 92677
90:41000025
35 00.966
$1.55 93.955009295
31 31.20000.991
35.94/00.956
70 63 63/0099923 9233
DIFESSR (Ours)
19.86M X2
31,69/ 00.9988
30,68 009327
36.61, 0.9669
31,84, 00.932
(0.9418
36.65 0.9607
30.27 00.9301
EDSR [5]
X4
38.9M
26 26,26/0.795-
25. 38,07811
29. 15 0.8883
26,35/0.0155
26.04/0.8888
29, 29.23,0.83 0.997
23 46/007228
RDN 29
x4
22.0M
26 23 (0. 7962
25.37 1077813
29.15 00.888
26 26.32/0..014
26.04/00.804
29. 20/8044
23.47 00.725
RCAN
X4
15.4M
26 36 7968
25.53/0
29 20/ 0.8881
26.44 8029
26 26.22/008006 8068
29. 30, 30/08337
23 73.4660077266
RtercoSR
1.4MM
23.67
27. 0.8000
24 24,55007555
24.21,0.7511
27.
21,70,00 6460
PASSRoet
1.4M
26.26/079919
(77777
28.6 61.882252
26.34/0.7981
06.08/0.8000
28.72/0.8256
SRRe#SAMI
x4
1.73M
26 /6.777
25.55
25.55,007825
28. 28.76/0.8287
26 26,04/0.8018
26.22/0.8054
28.83/0.8290
23.27/0.7233
IMSSRnel 10
X4
6.89M
26.44/-
25. 59/-
29 29.02/-
26.43/-
26/20)
29.02/-
-/-
iPASSR 5
X4
1.42M
(0.793
(0.750
20.07 00.836
26 26.56/0.9053
26.32/0.8085
20. 10.116,1 00.367
23 44, 04.7277
SSRDE NNet
2.24M
26.61
8020
25.74/0.7884
29.29 0.8407
26 06.70/0.8082
26.43/0.8118
29.38 10.8411
23. 59, 00.7352
NAFSSR-1
0.46M
2015
(5.90
29.2 0.
26.79/0.8100
29. 29.32/0 1.199
69/0.7384
NAPSSR-S
x44
1.56M
8086
26.03/0.7978
29.62 01 8482
26.93/008845
26. 8,,203
29. 20.722008499
7469
NAFSSR-BO
x4
6.80M
26.99/0.1211
25. 17/10,0000
29. 0.856
27.08,00.8188
25.91 82,245
30.04, 0.8568
24 07 /0.755
NAFSSR-LD
x4
23.83M
27.01/0.8135
26.22/00.9035
30.11 00.861
27. 27.12/00.899
26. 03.96/00 26.96/0.8257
30.20 00.600
24.17/0.7580
SCGLANer 34
x4
25.29M
27.003 (08154
26.15/008055
30.23 00.8627
27. 27.10/008300
26.87 80.663
30.04 00.868
24 10/06667767
Steformer [9]
1.34M
26.61, /0.077
25.74/0.7906
29.29 0.88444
26.70/0.8098
26.45/0.8134
29. 10.845
23.58 (0.736
SW
x4
24.09M
27.06/0.8155
26.15/0.8062
00.33/0.8676
27.16/00.825
26.89/0.8288
30.44 00.667
24.29/0.7681
(35
x4
1.82M
26.38 8098
25.07
70.990
0.886
26.970088 1558
26. 26.82/008219
29.77/0.8500
23.59/0.775 00.7508
DIFPSSR- (Ours)
x4
1.69M
26.90/0.8122
26.05 80.802
29. 88, 0.8881
25.00/0.8191
26. 78,00
30.00/0.8858
24 04/0.7569
DIFFSSR (Ours)
x4 20.01M
27.15 0.8201
26.23 0.8092
30.55 0.8723
27.25/0.8261
26.98, 0.8312
0.872
24.40/0.7723
Table 2: Comparison of perceptual quality on the real-world dataset Ste20oWeb20 335
Dataset
Metric
NAFSSR 6
SCGLANet-GAN
DIFFSSR-GAN (Ours)
NIQEL
5.7363
4.4174
4.1831
MANIQA
0.4648
0.5761
0.6184
SterooWeb20 35
MUSIQU
46.78
62.18
63.67
CLIPIQAT
0,5144
0.6331
0.6857
we employ a Charbonnier L1 loss function (2 to measure the difference between the super-resolved
and ground-truth stereo images. The model is trained on four NVIDIA GeForce RTX 3090 GPU, and the training process is carefully monitored to ensure convergence and to adjust hyperparameters as necessary for optimal performance.
4.2 Comparisons with State-of-the-art Methods
Quantitative Evaluations. Table I presents a comprehensive quantitative comparison of various methods for stereo image super-resolution (StereoSR) on the KITTI 2012, KITTI 2015, Middlebury, and Flickr1024 datasets. The metrics used for evaluation are the Peak Signal-to-Note -oiise Ratio (PSNR) and Structural Similarity Index (SSIM), which are standard measures for assessing the quality of super-resolved images. The table reports these values for both individual left images and the average of left and right images in stereo pairs, providing holistic view of performance across different scales. SwinFIRSSR, an extension of SwinFIR, incorporates Fast Fourier Convolution (FFC) to enhance the model's ability to capture global information, achieving state-of-the-art (SOTA) performance in StereoSR. However, as indicated in Table [ our method, DIFFSSR, outperforms SwinFIRSSR across multiple benchmark datasets, including KITTI 2012, KITTI 2015, Middlebury, and Flickr 1024, for both x2 and x4 scaling factors. Specifically, at a x2 scale, DIFFSSR achieves PSNR of 30.27dB and SSIM of 0.9301 on the Flickr1022 dataset, surpassing SwinFIRSSR's s 30. 14dB and 0.9286, respectively. This trend continues at a x4 scale, where DIFFSSR garners PSNR of 24.40dB and an SSIM of 0. 0.7722, compared to SwinFIRSSR's 's 24.29dB and 0. 1.768 DIFFSSR demonstrates its superiority over NAFSSR-L. as well. At a scale of x2, NAFSSR-L achieves a PSNR of 31. 31.45dB and SSIM of 0.9261 on the KITTI 2012 dataset, which is lower than DIFFSSR's s scores. This gap widens at scale of x4, where DIFFSSR's 's PSNR and SSIM on the Middlebury dataset are
7

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ing (Left)
NAFSSR-L
SwinFIRSSR
DIFTSSR(our))
Reference
img_ 0003 (Right)
LR
NAFSSR-L L
SwinFIRSSR
DIFFSSR(Ours)
Reference
Figure 4: Visual results (x4) achieved by different methods on the Flickr1024 dataset.
significantly higher than NAFSSR-L's, 30.55dB vs. 30.11dB and 0.8723 vs. 0.8601, respectively. These results underscore the superior performance of DIFESSR reconstructing high-quality images from degraded stereo pairs. Another significant advantage of DIFFSSR is its parameter efficiency. As indicated in Table DIFFSSR has 19.86M parameters at a x2 scale and 20,01M parameters at a x4 scale, which is considerably fewer than SwinFIRSSR 23 .4M and 24.09M parameters, respectively. This parameter reduction does not compromise performance; in fact, enhances it.
Visual Comparison. Figure presents a qualitative assessment of the stereo image super-resae resolution performance three methods: NAFSSR, SwinFIRSSR, and our proposed DIFFSSR. The restoration of texture details is a critical aspect of super-resolution tasks, as fine details are often the first to be lost during the downscaling process.
demoonstaasss that our DIFFSSR method outperforms both NAFSSR and SwinFIRSSR in this regard. The images produced by DIFFSSR exhibit sharper and more refined textures compared to the other two methods. the visual results, evident that NAFSSR, while effective in enhancing image resolution, tends to blur finer details, particularly in areas with complex textures. SwinFIRSSR shows some improvement over NAFSSR in preserving details, but it still falls short when compared to DIFFSSR. The images generated by SwinFIRSSR occasionally exhibit a loss of fine structures, suggesting that traditional Transformers may have issues with attention noise, leading the aggregation of large amount of irrelevant information, which results in suboptimal reconstruction and over-smoothing problems. In contrast, DIFFSSR demonstrates remarkable ability recover even the most subtle texture details. This is attributed to the differential attention mechanism within the Diff' Cross Attention Block (DCAB), which effectively differentiates between relevant and irrelevant context, thereby amplifying attention to important features and canceling out noise.
The superiority of our method can be attributed the innovative design of the DCAB and SSCAM. The DCAB's differential attention mechanism is adept at distinguishing between relevant and ir- relevant context, thus reducing attention noise. This, in turn, prevents over-smoothing by ensuring that the model focuses on significant features rather than dispersing attention across less relevant areas. The SSCAM, with its sliding window approach, enables more localized and adaptive feature integration, which is crucial for preserving texture details and preventing over-smoothing.
Real- World Evaluation. As shown in Table 2 our DIFFSSR-GAR consistently achieves the best perceptual quality across all metrics on the real- weal-world dataset Sth2eoWeh20. Specifically, it attains the lowest NIQE (4.1831) 1) and the highest MANIQA (0.6184), MUSIQ (63.67), and CLIPIQA (0.6857).

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outperforming both NAFSSR and SCGLANet-GAN, by notable margin. It is worth noting that SCGLANet-GAN was trained for 400,000 iterations with a batch size of 8 GPUs, whereas our DIFFSSR-GAN was trained for the same number of iterations but with smaller batch size of on only 4 GPUs, demonstrating better efficiency and robustness under more constrained computational resources, Moreover, our dataset simulation process follows the same degradation model as Real- ESRGAN, ensuring realistic noise and blur patterns that closely mimic real-world stereo imaging conditions. These results collectively validate that DIFFSSR-GAN effectively generalizes to real- world scenarios and achieves superior perceptual quality with less computational cost.
Table Comparison results under different Table 4: The comparison results with Swin- window size in Sliding Windows Stereo Cross-
FIRSSR. DIFF denotes replacing its Transformer with the DIFF Transformer.
Attention Module.
Window Size Flickr1024
KITTI 2012
KITTI 2015
Middlebury
Method
Phicr1022 KITTI 2012
KITTI 2015
Middlebury
24.35
27.23
26.94
30.57
SwinFIRSSR
24.29
27.16
26.89
30.44
27.24
26.71
5
24.40
27.25
26.98
30.64
DIFESSR
24.40
27.25
26.98
30.64
7
24.42
27.27
27.00
30.65
Table 5: Comparison results under different Positional Encoding (PE) and
Neural
Network (FFN). RoPE [2I] and RPE (7] denote Rotary Position Embedding and Relative Positional
Encoding, respectively. Depth denotes 0.8 - 0.6 * 0.3+deephh
Method Factor Flickr1024
KITTI 2012
KITTI 2015
Middlebury
24.10
26.96
26.69
30.13
PE
RoPE
24.11
26.99
26.71
30.14
RPE
24.13
26.99
26.72
30.17
SwiGLU
24.11
26.99
26.71
30.14
FEN
ML.P
24.18
27.06
26.85
30.31
0.8
24.11
26.99
26.71
30.14
Ainit
Depth
24.11
26.99
26.71
30.12
Learnable
24.11
26.99
26.71
30.14
4.3
Ablation Study
4.3.1
Impact of the Window Sizes
The ablation study results presented in Table offer valuable insights into the impact of varying window sizes within the Sliding Stereo Cross-Attention Module (SSCAM), It is observed that when the window size is increased from to 3, there is significant and consistent improvement in PSNR test datasets. This suggests that moderate window size of 3 already captures additional contextual information beneficial for super-resolution tasks. Further increasing the window size to 5 results in marginal improvements over the window size of 3. with PSNR values of 24.40dB, 27.25 dB, 26.98 dB, and 30.64 dB on Flickr1024, KITTI 2012, KITTI 2015, and Middlebury datasets, respectively. While the window size of yields the highest PSNR values across datasets, its excessive computational requirements make it impractical. It is worth noting that odd-numbered window sizes (1, 5, 7) are adopted due to their inherent symmetry and implementation simplicity, which are common and reasonable design choices in visual models. The window size of 3 provides near-optimal balance, offering significant performance gains with reasonable computational costs, making it the chosen configuration for our model.
4.3.2 Impact of the DIFF Transformer
The ablation study in Table reveals that directly replacing the Transformer in SwinFIRSSR with the DIFF Transformer (referred to as the DIFF) leads to a decrease in PSNR across all datasets, indicating that the DIFF Transformer does not translate its benefits from language tasks to visual tasks effectively, In contrast, our proposed DIFFSSR, which incorporates a redesigned DIFF Transformer architecture tailored for stereo super-resolution, outperforms both SwinFIRSSR and the DIFF methods. This enhancement underscores the importance of architecture design in leveraging the complementary information present stereo image pairs and highlights the potential the DIFF Transformer when adapted appropriately for visual tasks.

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4.3.3 Impact of Some Factors in Transformer
The ablation study presented in Table 5 provides a detailed analysis of the impact of different Positional Encodings (PE) and
Neural Network (FFN) architectures on the performance of the DIFF Transformer model. The first group of rows in the table compares the performance of three different positional encoding methods. The results indicate that positional encoding consistently improves performance across all datasets. However, the analysis also suggests that while RoPE has visual
shown significant benefits in L.Mss, may not effectively capture the spatial relationships data, indicated by the relatively small improvement in performance with RoPE compared to RPE. This aligns with prior findings suggesting that RoPE may not be the most suitable for visual tasks that rely heavily on relative spatial relationships, where RPE offers more fitting approach. The second group of rows compares the performance of two different FFN architectures. MLP outperforms SwiGLU across all datasets, yielding improvements of 0.07, 0.07, 0.14, and 0.17 for the respective datasets. This suggests that MLP is more suited for this task, potentially due to its ability to model more complex non-linearities and interactions between features, which may be particularly beneficial in tasks that require higher levels of abstraction or richer feature representations.
Conclusion
In this paper, introduced DIFFSSR, novel approach stereo image super-resolution that leverages the strengths of the Differential Transformer to address the limitations of traditional Transformer models. Through the innovative design of the Diff Cross Attention Block (DCAB) and the Sliding Stereo Cross-Attention Module (SSCAM), DIFFSSR effectively reduces attention noise and improves the integration of crews-view information, leading superior performance in recovering fine texture details and preventing over-reconstruution. Our method achieved state-of-the- art results across multiple benchmark datasets, outperforming existing methods such as NAFSSR and SwiRFIRSSR. The visual comparison clearly demonstrates DIFFSSR'S ' ability to restore low- resolution images with greater accuracy and detail, while also avoiding the over-smoothing artifact that plague traditional Transformer-bas models. The success of DIFFSSR highlights the importance of tailored neural network architectures for specific visual tasks and the potential of Differential Transformer in improving the performance of super- resolutino models.
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A Additional Ablation Study
A.0.1 The Effectiveness of SSCAM
As shown in Table for the ablation study of SSCAM, we replaced the SSCAM with SCAM in the Diff Cross Attention Layer. The experimental results demonstrate that our proposed SSCAM achieves superior performance compared to SCAM. To further validate the effectiveness of SSCAM. we simulated misalignment between left and right views by randomly applying vertical shifts of 0-3 pixels to the right view in the Flickr1024 validation dataset. We evaluated only the PSNR of the left view in this setup. The results indicate that our SSCAM incurs negligible performance degradation, while the methods based on SCAM (SwinFIRSSR and NAFSSR) exhibit significant drop performance.
Table 6: The effectiveness of our SSCAM.
Method Flickr1024
DIFFSSR
DIFFSSR(SCAM)
SwinFIRSSR
NAFSSR 24.11
24.34
24.27 24.05(-0.22)
24.22
Flickr1024 + vertical shift 24.31(-0.03)
23.98(-0.24)
23.74(-0.37)
A.0.2 The Visualization of the Attention Map
As shown Figure through the visualization attention maps, it can be observed that self-attention mechanisms tend to assign higher weights irrelevant pixels, while differential attention (our) demonstrates the capability to effectively mitigate noise interference. Furthermore, comprehensive theoretical analyses and experimental validations of the noise suppression capacity inherent in differential attention have been rigorously demonstrated by the original authors.
westaves 1018
Salf-attentoon
Differential Attention (org)
Differential Attention
Figure 5: The visualization of the attention map.
A.0.3 Impact of the Window Sizes
The Table 7 presents a comprehensive analysis of the impact of varying window sizes on the performance of the Sliding Stereo Cross-Attention Module in stereo image super-resolution. The results indicate that as the window size increases from to 7, there is a corresponding improvement in performance such PSNR and SSIM across different datasets. Specifically, the largest window size 7 yields the best performance, with PSNR values of 24.42dB, 27. 27, 27. 27.00dB, and 30.65dB respectively. However, this enhancement in performance comes at a cost. The computational complexity, measured in GFlops, and the inference time both increase significantly with larger window sizes. For example, while a window size of has a computational cost of 137.42 GFlops and an inference time of 0.1003 seconds, these values rise to 435.54 GFlops and 0.1683 seconds for a window size of Despite the superior performance of the largest window size, the study concludes that a window size of 3 strikes an optimal balance between performance and computational efficiency. This size offers a reasonable trade-off, with a moderate increase in computational cost (187.11 GFlops) and inference time (0.1274 seconds) compared to the baseline, while still achieving notable performance gains. This makes a practical choice for real- world applications where both performance and efficiency are critical considerations.
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Table 7: Comparison results under different window size Sliding Windows Stereo
Module. We test our method on NVIDIA GeForce RTX 3090 GPU with the resolution 32 x6.
Window Size
#P
GFlops
T Time
Flickr1024
KITTI 2012
KITTI 2015
Middlebury
20.01
137.42
0.1003s
24.35
27.23
26.94
30.57
20.01
187.11
0,1274s
24.39
27.24
26.97
30.64
20.01
286.48
0.1442s
24.40
27.25
26.98
30.64
20.01
435.54
0.1683s
24.42
27.27
27.00
30.65
A.0.4 Training Free for the Sliding Stereo Cross-Attention Module (SSCAM)
The experimental results presented in Table 8 demonstrate the critical advantage of the proposed SSCAM in achieving training-free adaptability varying window sizes during inference, capability lacking in the NAFSSR's s SCAM. Specifically, when trained with a window size of and tested with larger window sizes (3 5), SSCAM-based DIFFSSR consistently improves performance across all datasets, whereas NAFSSR suffers significant degradation. For instance, on the Flickr1024 dataset, NAFSSR's s PSNR drops from 24.16 dB (test window=1) to 24. 13 dB (test windows=3), indicating its sensitivity to window size mismatches. In contrast, DIFFSSR trained with window=1 cxhibits a performance gain from 24.35 dB to 24.37 dB when tested with larger windows, highlighting SSCAM's intrinsic ability to exploit expanded spatial contexts without retraining. Similar trends are observed on KITTI and Middlebury datasets, where DIFFSSR maintains slightly enhances accuracy regardless of test window sizes, while NAFSSR's scores consistently decline. Furthermore, DIFFSSR exhibits remarkable robustness even when trained with larger windows (3 5). For example, DIFFSSR trained with window=5 achieves 24.40 dB on Flickr 1024 when tested with window=5, marginally outperforming its window=3 training counterpart (24.39 dB). This suggests SSCAM's bidirectional compatibility-ij not only enables upward window size generalization from smaller training windows but also maintains stability when trained with larger windows, such versatility is absent in NAFSSR.
Table 8: Comparison results under different window size in Sliding Windows Stereo Cross-Attention Module.
Method Train Window Size
Test Window Size
Flickr1024
KITTI 2012
KITTI 2015
Middlebury
24.16
27.11
26.94
30.19
NAFSSR
24.14
27.09
26.92
30.18
24.13
27.07
26.91
30.16
24.35
27.23
26.94
30.59
DIFFSSR
24.37
27.23
26.97
30.59
24.37
27.23
26.97
30.59
24.37
27.24
26.96
30.61
DIFFSSR
3
24.39
27.24
26.97
30.64
24.39
27.24
26.97
30.64
24.36
27.24
26.96
30.60
DIFFSSR
24.39
27.25
26.97
30.64
24.40
27.25
26.98
30.64
A.0.5 Parameters vs. PSNR
The Figure 6 provides Frouidee a comparison of various stereo image super-resolution methods, highlighting the trade-off between the number of parameters and PSNR performance on the Flickr1024 dataset. Our methods, DIFFSSR and DIFFSSR-T demonstrate significant improvements in performance and parameter efficiency. Specifically, DIFFSSR achieves SOTA performance with PSNR of 24.40dB (x4), surpassing other methods including NAFSSR-L (24.17 dB) and SwinFIRSSR (24.29 dB). Notably, DIFFSSR requires fewer parameters than both NAFSSR-L (6) and SwinFIRSSR 4.. x4 scale, DIFESSR has 20.01M parameters versus 23.83M for NAFSSR-L and 24.09M for Swin- FIRSSR, representing reductions of 16.0% and 16.9% respectively. DIFFSSR-T achieves comparable performance to NAFSSR-B but requiring fewer parameters (1.69M), 75.1 % reduction compared to NAFSSR-BB B's 6.80M. Additionally, DIFFSSR-T surpasses NAFSSR-S (1 (1.56M parameters) PSNR (24.04 dB vs. 23.88 dB), demonstrating 0.16 dB improvement despite similar parameter counts.
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30.5
Suntasss La 27PEU22
Tasta
24.2
(40) am 1ilck3322
20.75
POUR (ont on Rickero0
24.0
24.00
(C E3om 2072)
23.8
Maetomer
29
29.00Ner500
(hagee211
Steformer
53ho2 FRLE
Sonog Flat
25.0
78
EDSH Ec0 (CPPgg207)
PAASR
2.19
(CSPA2211)
(CVeR2023)
(SI (SP1202)
18"
107
10'
Number of Paramaters (4) (log-stale)
Number of Panamatiass (M) (lagstale)
(a) x2 super resolution
(b) x4 super resolution
Figure 6: The trade-off between the number of parameters and PSNR the Flickr1024 dataset.
A.0.6 The Comparison with Some More Recent SOTA Methods.
The FigureBaakks provides a comparison of various recent stereo image super-resolution methods. Although SwinFIRSSR [4] was initially proposed in 2022, it has consistently maintained SOTA performance. Therefore, select SwinFIRSSR the primary baseline for comparison. According to the NTIRE 2023 Challenge on Stereo Image Super-Resolution [36] results, SwinFIRSSR won the championship in Track 2. Furthermore, the NTIRE 2024 Challenge on Stereo Image Super- Resolution (37] results demonstrate that the tiny version of SwinFIRSSR achieved dual championships in both Track and Track In contrast, other methods exhibited inferior performance: CANSSR (88) ranked 6th in Track 1, MFFSSR [3 ranked 7th in Track I and 9th in Track 2. Notably, our proposed DIFFSSR surpasses SwinFIRSSR across four benchmark datasets, which provides compelling evidence for the effectiveness of our methodology. Our DIFFSSR also achieves higher performance than them.
B Limitations and Future Work
This work represents the first exploration of the Differential Transformer in visual tasks, specifically focusing on stereo image super-resolution (StereoSR). While the proposed DIFFSSR achieves state- of-the-art performance in StereoSR, its current design is tailored exclusively for this specific task. The adaptability and generalizability of the Differential Transformer to other visual domains (e.g.. image classification, object detection, semantic segmentation) remain unexplored. Additionally, the computational complexity of the Sliding Stereo Cross-Attention Module (SSCAM) increases with larger window sizes, which may affect real-time applications despite the optimal balance achieved with window size of 3.
Future work will focus generalizing the DIFF Transformer framework to diverse vision tasks. For instance, investigating its potential in image classification by adapting the differential attention mechanism to hierarchical feature learning, exploring its use in dense prediction tasks like detection and segmentation, could validate its broader utility.
C Additional Visual Results
In this part, we provide additional visual results compared the SOTA method.
22

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LR
NAFSSR-L
SCGLANet
img 0029 (Left)
SwinFIRSSR Figure 7: Visual results (x4) achieved by different methods on the Flickr1024 dataset.
Referencc
DIFFSSR(
LR
NAFSSR-L
SCGLANet
img 0033 (Leff))
Reference
SwinFIRSSR
DIFFSSR(Ours)
Figure 8: Visual results (x4) achieved by different methods on the Flickr1024 dataset.
23

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LR
NAPSSR-L
SCGLANct
img 0034 (Left)
SwinFIRSSR Figure 9: Visual results (x4) achieved by different methods on the Flickr1024 dataset.
Reference
DIFFSSR(ours)
img 000000 (Left)
LR
NAFSSR-L
SCGLANet
Reference
SwinFIRSSR
DIFFSSR(Ours)
Figure 10: Visual results (x4) achieved by different methods on the KITT12012 dataset.

---

Bicubic
SterenSR
EDSR
RCAN
SRRE
img 0035 (Left)
iPASSR
NAFSSR-L
SWinFIRSSR
DIFrS)R(uus))
Refereace
Bicubic
SterenSR
EDSR
RCAN
SRRes+SAM
inn (Right)
DIFFSSR(OUS)) Figure 11: Visual results (x4) achieved by different methods on the Flickr1024 dataset.
iPASSR
NAFSSR-L
SwinFIRSSR
Reference
25


Reviews:
[{"review_id": "4IkAb5iYbC", "reviewer": "Reviewer_6cnT", "summary": "This paper proposes DIFFSSR, which transfers the Differential Transformer to stereo image super-resolution. It pairs a Diff Cross Attention Block that keeps useful detail and reduces noise with a Sliding Stereo Cross-Attention Module that scans left and right views to fix misalignment. DIFFSSR beats SwinFIRSSR and NAFSSR in both scores and visual quality.", "questions": "Please refer to the weaknesses.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1.  Visual comparisons demonstrate superior texture preservation and reduced over-smoothing relative to the baselines.\n\nWeaknesses:\n1. Limited novelty: DCAB and SSCAM integrate established concepts such as Differential Transformer, windowed self-attention and cross-view attention, without new improvements for stereo tasks.\n2. Heavy computation with incomplete efficiency analysis: Even with a small window, SSCAM incurs about 40 % more FLOPs than the variant without the window, which can hinder real-time deployment. Besides, this paper does not report FLOPs for competing methods.\n3. Incremental experimental gains: Compared to competing methods with comparable parameter counts, DIFFSSR yields < 0.1 dB improvement on most datasets, indicating minimal practical advantage.\n4. Excessive mathematical notation: The manuscript contains 25 equations, many of which are not essential to the core derivation and could be removed.", "was_revised": true, "final_justification": "While the method shows some empirical gains, its core components are largely built on existing modules, with limited evidence of true methodological novelty. Some experimental comparisons, such as on the Middlebury dataset, may be biased due to differences in training data. Key design changes like replacing the Unfolding operation were not part of the original paper and lack sufficient justification. Overall, my concerns regarding novelty, fairness, and completeness remain unresolved.", "rebuttal": "We sincerely thank you for reviewing our paper and providing us valuable feedback. We have addressed your concerns as below.\n\n1. Limited novelty: DCAB and SSCAM integrate established concepts such as Differential Transformer, windowed self-attention and cross-view attention, without new improvements for stereo tasks.\n\n   > A1: a. Our DCAB is the first work to explore the applicability of DIFF Transformer in visual task, specifically optimized for stereo image super-resolution (SISR), please see Sec. 3.2 for details. b. SSCAM solves the performance degradation problem caused by misalignment of horizontal epipolar line in stereo images. This design has never appeared in existing methods and is a specialized optimization for SISR. c. In summary, our DCAB and SSCAM are not simply integrated concepts, but specific improvements proposed for SISR, and experimental results also demonstrate the effectiveness of our method (Figure 4 and Table 1).\n\n2. Heavy computation with incomplete efficiency analysis: Even with a small window, SSCAM incurs about 40 % more FLOPs than the variant without the window, which can hinder real-time deployment. Besides, this paper does not report FLOPs for competing methods.\n\n   > A2: a. SSCAM is training free and can be trained in large windows while reasoning in the original window, improving performance without increasing inference time, as shown in Table 7. b. For real-time issues, a mixed window design can be used, such as using a 3x3 window for the first layer DCAL in each DCAB and a 1x1 window for the rest. The performance did not decrease in Flickr1024, while Flops decreased from 187.11 to 153.98. c. We have reconsidered the Unfolding operation and believe that we can use a window shift method similar to swin transformer instead of Unfolding. This way is training free and can reduce duplicate calculations and be more efficient, please see the Table. Although other operations can be used instead of the Unfolding operation, the core idea of our method remains unchanged. d. As reviewer JZxY pointed out, misalignment is a very common issue. And we only randomly shifts 0-3 pixels, resulting in a significant decrease in the performance of existing SCAM, as shown in Table 5. And our method maintains considerable performance, indicating that SSCAM is very necessary. e. We are going to add comparison of FLOPs with mainstream methods such as SwinFIRSSR and SCGLANet.\n   >\n   > |           Window size            | PSNR  | Flops  |\n   > | :------------------------------: | :---: | :----: |\n   > |  DIFFSSR (win=1, Unfolding op)   | 24.35 | 137.42 |\n   > | DIFFSSR (win=3-1, Unfolding op)  | 24.39 | 153.98 |\n   > |  DIFFSSR (win=3, Unfolding op)   | 24.39 | 184.11 |\n   > | DIFFSSR (win=1, window shift op) | 24.36 | 137.42 |\n   > | DIFFSSR (win=2, window shift op) | 24.40 | 143.63 |\n   > |            SwinFIRSSR            | 24.29 | 147.39 |\n   > |             SCGLANet             | 24.30 | 138.83 |\n   >\n   > \n\n3. Incremental experimental gains: Compared to competing methods with comparable parameter counts, DIFFSSR yields < 0.1 dB improvement on most datasets, indicating minimal practical advantage.\n\n   > A3: DIFFSSR achieves a PSNR improvement of 0.23 dB over NAFSSR-L on the Flickr1024 dataset (vs. 0.13 dB for SCGLANet and 0.12 dB for SwinFIRSSR), and 0.44 dB on Middlebury (vs. -0.16 dB for SCGLANet and 0.24 dB for SwinFIRSSR). These improvements are considered significant in the super-resolution field, especially the visual improvement is even more significant, as shown in Figure 4.\n\n4. Excessive mathematical notation: The manuscript contains 25 equations, many of which are not essential to the core derivation and could be removed.\n\n   > A4: As you suggest, we are going to remove equations that do not affect understanding of implementation details."}, {"review_id": "X9Zbg5yhm6", "reviewer": "Reviewer_CHFU", "summary": "This paper proposes DIFFSSR, a stereo image super-resolution framework that adapts the Differential Transformer to visual tasks. To address attention noise and stereo misalignment, the authors introduce two modules: the Diff Cross Attention Block (DCAB) and the Sliding Stereo Cross-Attention Module (SSCAM). Extensive experiments on standard benchmarks demonstrate that DIFFSSR outperforms prior methods such as SwinFIRSSR and NAFSSR in both accuracy and parameter efficiency.", "questions": "1. Since the method is built on the Differential Transformer, the paper would benefit from a brief preliminaries section summarizing its core ideas and formulations. This would help readers better understand the motivation behind the proposed architectural modifications.\n2. The paper should clearly state that the effectiveness of the Differential Transformer comes from constructing opposing attention heads and using their difference to highlight relevant features while suppressing background noise. This design motivation is important for understanding how it addresses attention noise. Additionally, Section 3.3 should better explain why SSCAM is effective for handling stereo misalignment.\n3. Section 4.3.1 discusses the impact of window sizes 1 to 3, but Table 2 does not include results for sizes 1 and 2. These should be reported for completeness. In addition, Table 4 should include a comparison between window-based multi-head differential attention and standard multi-head differential attention, as this is one of the three core modifications.\n\nIf the authors can adequately address the above questions and resolve the concerns outlined in the weaknesses section, I would be willing to reconsider my overall assessment.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. The paper is well organized and clearly written, making it easy to understand the motivation, proposed method, and the role of each component.\n\n2. This paper presents the first attempt to adapt the Differential Transformer to visual tasks and proposes two modules, DCAB and SSCAM, which effectively reduce attention noise and address stereo misalignment.\n\n3. The method achieves competitive performance on multiple standard benchmarks, with comprehensive comparisons against SOTA baselines, showing consistent improvements in both accuracy and efficiency.\n\n\nWeaknesses:\n\n1. Although the paper introduces two new modules, their design is relatively incremental. Both DCAB and SSCAM are based on known concepts such as attention refinement and sliding window mechanisms, and may not be considered highly novel from an architectural perspective.\n\n2. While the paper provides high-level motivation for DCAB and SSCAM, it lacks deeper analysis or justification of why these specific designs are effective in addressing attention noise and stereo misalignment. The theoretical or intuitive reasoning behind the effectiveness of the modules could be elaborated further.\n\n3. There are several minor errors and unclear points in the paper. For example, the red, blue, and green boxes in Figure 3(b) are not explicitly explained. Equations (13) and (14) use incorrect input notations, and some subscripts or superscripts are inconsistent with those shown in Figure 3(b). Additionally, the meanings of the superscripts L and R in Section 3.1 should be clearly defined at the beginning of the section.", "was_revised": true, "final_justification": "The authors have clarified the novelty in the rebuttal, which, while still relatively modest, is acceptable. I hope they will, as promised, include more theoretical motivation in the final version to better highlight the contributions. Therefore, I am willing to raise my rating.", "rebuttal": "We appreciate the constructive comments on our paper. The raised concerns are addressed as follows.\n\n1. Although the paper introduces two new modules, their design is relatively incremental. Both DCAB and SSCAM are based on known concepts such as attention refinement and sliding window mechanisms, and may not be considered highly novel from an architectural perspective.\n\n   > A1: a. Our DCAB is the first work to explore the applicability of DIFF Transformer in visual task, specifically optimized for stereo image super-resolution (SISR), please see Sec. 3.2 for details. b. SSCAM solves the performance degradation problem caused by misalignment of horizontal epipolar line in stereo images. This design has never appeared in existing methods and is a specialized optimization for SISR. c. In summary, our DCAB and SSCAM are not simply integrated concepts, but specific improvements proposed for SISR, and experimental results also demonstrate the effectiveness of our method (Figure 4 and Table 1).\n\n2. While the paper provides high-level motivation for DCAB and SSCAM, it lacks deeper analysis or justification of why these specific designs are effective in addressing attention noise and stereo misalignment. The theoretical or intuitive reasoning behind the effectiveness of the modules could be elaborated further.\n\n   > A2: a. In a traditional Transformer, the attention mechanism distributes focus across all tokens in a sequence using a single softmax function. This behavior over-smoothing the results when the Transformer gives unnecessary weight to distracting tokens. Instead of relying on a single attention map, DIFF Transformer computes two softmax maps: One focuses on relevant tokens; The other represents noise or distractions in the input. The difference between the two maps becomes the final attention score, removing shared distractions while amplifying the signal that matters. This mechanism aligns with the idea of filtering out common-mode noise, allowing the model to efficiently focus on key information.  And Sec. A.0.2 intuitively indicate that DIFF can effectively reduce attention noise compared to self attention. b. As you can see from the principle of SCAM and Figure 2, SCAM can not solve the problem of misalignment of horizontal epipolar line. Our SSCAM addresses this problem by sliding window mechanism and parallax attention. To validate the effectiveness of SSCAM, we simulated misalignment between left and right views by randomly applying vertical shifts of 0-3 pixels to the right view in the Flickr1024 validation dataset. We evaluated only the PSNR of the left view in this setup. The results indicate that our SSCAM incurs negligible performance degradation, while the methods based on SCAM (SwinFIRSSR and NAFSSR) exhibit a significant drop in performance (see Sec. A.0.1).\n\n3. There are several minor errors and unclear points in the paper. For example, the red, blue, and green boxes in Figure 3(b) are not explicitly explained. Equations (13) and (14) use incorrect input notations, and some subscripts or superscripts are inconsistent with those shown in Figure 3(b). Additionally, the meanings of the superscripts L and R in Section 3.1 should be clearly defined at the beginning of the section.\n\n   > A3: As you suggest, we are going to add instructions and uniform symbolic representations. Red is layer normalization, blue is window-based multi-head differential attention module, green is multilayer perceptron. $F_L$ and $F_R$ in  Figure 3 should be $F^L$ and $F^R$. L and R are left and right images.\n\n4. Since the method is built on the Differential Transformer, the paper would benefit from a brief preliminaries section summarizing its core ideas and formulations. This would help readers better understand the motivation behind the proposed architectural modifications.\n\n   > A4: As you suggest, we are going to add a brief preliminary to summarize the DIFF Transformer and our research motivations.\n\n5. The paper should clearly state that the effectiveness of the Differential Transformer comes from constructing opposing attention heads and using their difference to highlight relevant features while suppressing background noise. This design motivation is important for understanding how it addresses attention noise. Additionally, Section 3.3 should better explain why SSCAM is effective for handling stereo misalignment.\n\n   > A5: a. Thank you for your suggestion. As you understand, that is exactly our core research motivation. Excessive fusion of irrelevant information can reduce the quality of image super-resolution, and this negative impact can be mitigated through the use of DIFF Transformer. b. We will add a legend to further illustrate the working principle of SSCAM.\n\n6. Section 4.3.1 discusses the impact of window sizes 1 to 3, but Table 2 does not include results for sizes 1 and 2. These should be reported for completeness. In addition, Table 4 should include a comparison between window-based multi-head differential attention and standard multi-head differential attention, as this is one of the three core modifications.\n\n   > A6: a. \"-\" indicates window size=1. The window sizes we designed are all odd values, so there are no experimental results with window size=2. b. The \"CUDA out of memory\" error on the 3090 when run standard DIFF indicates that it is not suitable for visual tasks. We optimized it to run efficiently on low performance GPUs."}, {"review_id": "UQT3VxRR99", "reviewer": "Reviewer_o4ZS", "summary": "In this paper, the authors develop a stereo image SR network on top of differetial Transformer. As the attention noise issue in Transformers is well acknowledged and results in inferior performance, this paper leverages differential Transformer to alleviate this issue for higher accuracy. However, the authors have observed that directly applying differential Transformer to stereo image SR produces performance degradation. To remedy this, Diff Cross Attention Block and Sliding Stereo Cross-Attention Module are developed to enhance feature integration. Extensive experiments are conducted on benchmark datasets, and the results have demonstrated the superiority of the proposed method over previous approaches. In addition, an ablation study is also conducted to validate the effectiveness of the proposed network designs.", "questions": "Please see weaknesses.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The motivation is clear, and the proposed method is well presented.\n\n- State-of-the-art performance is achieved by the proposed method.\n\n\nWeaknesses:\n- The major concern is that the technical contribution may seem to be limited at first glance. The major network designs (i.e., DCAB and SSCAM) are built upon differential Transformer, and the authors have modified the original architectures to fit the stereo SR task. Besides, extensive experiments are conducted to demonstrate the effectiveness of these modifications. Overall, I think the observation that directly applying Diff produces inferior performance is valuable, and the experiments are solid.\n\n- In addition to the comparison in Fig. 5, it is recommended to include the original differential attention map to further highlight the gains introduced by the proposed modifications.\n\n- As generalization in real-world scenarios is a critical factor for image SR methods, it is recommended to evaluate the proposed method on real-world low-resolution stereo images and compare its results with previous SOTA methods.\n\n- In Fig. 1, it is not very clear to me why DIFF also suffers a performance drop on KITTI and Middlebury. It seems the vertical misalignment only exists in the Flickr1024 dataset.", "was_revised": true, "final_justification": "The rebuttal has addressed most of my concerns. After carefully reading the discussions between the authors and other reviewers, I decide to keep my rating.", "rebuttal": "We appreciate the comments on our paper. The raised concerns are addressed as follows.\n\n1. The major concern is that the technical contribution may seem to be limited at first glance. The major network designs (i.e., DCAB and SSCAM) are built upon differential Transformer, and the authors have modified the original architectures to fit the stereo SR task. Besides, extensive experiments are conducted to demonstrate the effectiveness of these modifications. Overall, I think the observation that directly applying Diff produces inferior performance is valuable, and the experiments are solid.\n\n   > A1: I really appreciate your approval of my work. a. Our DCAB is the first work to explore the applicability of DIFF Transformer in visual task, specifically optimized for stereo image super-resolution (SISR), please see Sec. 3.2 for details. b. SSCAM solves the performance degradation problem caused by misalignment of horizontal epipolar line in stereo images. This design has never appeared in existing methods and is a specialized optimization for SISR. c. In summary, our DCAB and SSCAM are not simply integrated concepts, but specific improvements proposed for SISR, and experimental results also demonstrate the effectiveness of our method (Figure 4 and Table 1).\n\n2. In addition to the comparison in Fig. 5, it is recommended to include the original differential attention map to further highlight the gains introduced by the proposed modifications.\n\n   > A2: Thank you for your suggestion. We will add the original diff attention map for comparison.\n\n3. As generalization in real-world scenarios is a critical factor for image SR methods, it is recommended to evaluate the proposed method on real-world low-resolution stereo images and compare its results with previous SOTA methods.\n\n   > A3: Thank you for your suggestion. We are currently writing the relevant training code (including dataload, training process), but it will take time for training. If the training can be completed before August 6th, we will report the corresponding results. As you said, generalization in real-world scenarios is a critical factor in image SR methods, and we will definitely add the experimental results of real-world scenarios.\n\n4. In Fig. 1, it is not very clear to me why DIFF also suffers a performance drop on KITTI and Middlebury. It seems the vertical misalignment only exists in the Flickr1024 dataset.\n\n   > A4: The main reason for the performance degradation of DIFF is that the native DIFF is designed for LLM, and some of its components are not suitable for visual tasks (please see ablation study in Sec. 4.3.3 and Table 4)."}, {"review_id": "1lYZmhFVCi", "reviewer": "Reviewer_JZxY", "summary": "The paper proposes a transformer-based algorithm for stereo super-resolution tasks. The algorithm redesigns the Differential transformer block to accommodate it in the vision task, and designs a cross-attention module with a sliding window that is more robust to inaccuracies in stereo rectification. The method achieves SOTA scores on benchmark datasets and demonstrates promising visual results.", "questions": "Add analysis/visuals to better justify how the diff transformer helps narrow down the focus to relevant pixels.\n\nThe Quantitative Evaluations section is quite verbose. There is no need to repeat the data in the table. Keep it concise and save some space for supportive visuals.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n* The method achieves SOTA scores on benchmark datasets and demonstrates promising visual results.\n* The idea of using unfolding op has a simple implementation but sounds effective against small misalignments.\n\nWeaknesses:\n* One of the motivations and contributions is to deal with attention noise. Yet, the impact of such noise in the vision or stereo SR task is not sufficiently analyzed in the main body. A.0.2 shows a sparser attention map, but how it connects to more relevant pixels is in question. \n* Unfolding op cannot handle large parallax that falls outside of the unfolding size. Large parallax is not uncommon in the scenes with rich and distinct depths, e.g. autonomous driving.\n* The numerical improvements are mostly less than 1% compared to SwinFIRSSR, which is considered marginal.", "was_revised": true, "final_justification": "The concerns of dealing with practical large parallax and scores reside within a close margin of existing methods remain. Thus I choose to keep my rating.", "rebuttal": "Thanks for your time and effort reviewing our paper. Below are our responses.\n\n1. One of the motivations and contributions is to deal with attention noise. Yet, the impact of such noise in the vision or stereo SR task is not sufficiently analyzed in the main body. A.0.2 shows a sparser attention map, but how it connects to more relevant pixels is in question.\n\n   > A1: a. In a traditional Transformer, the attention mechanism distributes focus across all tokens in a sequence using a single softmax function. This behavior over-smoothing the results when the Transformer gives unnecessary weight to distracting tokens. Instead of relying on a single attention map, DIFF Transformer computes two softmax maps: One focuses on relevant tokens; The other represents noise or distractions in the input. The difference between the two maps becomes the final attention score, removing shared distractions while amplifying the signal that matters. This mechanism aligns with the idea of filtering out common-mode noise, allowing the model to efficiently focus on key information.  And Sec. A.0.2 intuitively indicate that DIFF can effectively reduce attention noise compared to self attention.\n\n2. Unfolding op cannot handle large parallax that falls outside of the unfolding size. Large parallax is not uncommon in the scenes with rich and distinct depths, e.g. autonomous driving.\n\n   >A2: You're right, we agree with your point of view. a. For large parallax,  we think that using alignment algorithms to realign the views may be beneficial. However, these algorithms cannot achieve perfect alignment, resulting in residual misalignment. Existing SCAM-based methods suffer performance degradation due to this minor misalignment. As demonstrated in Table 5, even a random pixel offset of only 0-3 pixels causes a sharp performance drop in SCAM-based methods. Therefore, in large parallax scenarios, applying an alignment algorithm first to align the left and right views, and then applying our SSCAM, can further improve performance. b. We have reconsidered the Unfolding operation and believe that we can use a window shift method similar to swin transformer instead of Unfolding. This way is training free and can reduce duplicate calculations and be more efficient, please see the Table.\n   >\n   >|           Window size            | PSNR  | Flops  |\n   >| :------------------------------: | :---: | :----: |\n   >|  DIFFSSR (win=1, Unfolding op)   | 24.35 | 137.42 |\n   >|  DIFFSSR (win=3, Unfolding op)   | 24.39 | 184.11 |\n   >| DIFFSSR (win=1, window shift op) | 24.36 | 137.42 |\n   >| DIFFSSR (win=2, window shift op) | 24.40 | 143.63 |\n\n3. The numerical improvements are mostly less than 1% compared to SwinFIRSSR, which is considered marginal.\n\n   >A3: DIFFSSR achieves a PSNR improvement of 0.23 dB over NAFSSR-L on the Flickr1024 dataset (vs. 0.13 dB for SCGLANet and 0.12 dB for SwinFIRSSR), and 0.44 dB on Middlebury (vs. -0.16 dB for SCGLANet and 0.24 dB for SwinFIRSSR). These improvements are considered significant in the super-resolution field, especially the visual improvement is even more significant, as shown in Figure 4.\n\n4. Add analysis/visuals to better justify how the diff transformer helps narrow down the focus to relevant pixels.\n\n   > A4: Thank you for your suggestion. From Figure 5, it can be seen that DIFF attention sets the attention score of irrelevant pixels to zero, thereby avoiding the fusion of irrelevant information.\n\n5. The Quantitative Evaluations section is quite verbose. There is no need to repeat the data in the table. Keep it concise and save some space for supportive visuals.\n\n   > A5: Thank you for your suggestion. We will reduce duplicate data and outdated methods, and add more visual comparison results."}]

Author rebuttal:

Meta-review: This paper proposes an effective DIFFSSR that includes DCAB and SSCAM modules for stereo image super-resolution. Experimental results demonstrate that the effect of the proposed DIFFSSR.

The major concerns of this paper include the limited novelty and marginal performance improvement. Based on the provided rebuttal, Reviewer 6cnT still pointed out that the novelty of the paper is limited. Other reviewers are satisfied with authors' response. However, their recommendations are "Borderline accept".

If this manuscript is accepted, the authors should clarify the novelty.
Decision: Accept (poster)