Rethinking Expressivity and Degradation-Awareness in Attention for All-in-One Blind Image Restoration
Authors: Bin Ren
Runyi Yang
Qi Ma
Xu Zheng
Mengyuan Liu
Danda Pani Paudel
Luc Van Gool
Rita Cucchiara
Nicu Sebe
Venue: ICLR 2026 Poster (2026)
Keywords: Transformer
Image Restoration
Representation Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: All-in-one image restoration (IR) aims to recover high-quality images from diverse degradations, which in real-world settings are often mixed and unknown. Unlike single-task IR, this problem requires a model to approximate a family of heterogeneous inverse functions, making it fundamentally more challenging and practically important. Although recent focus has shifted toward large multimodal models, their robustness still depends on faithful low-level inputs, and the principles that govern effective restoration remain underexplored. We revisit attention mechanisms through the lens of all-in-one IR and identify two overlooked bottlenecks in widely adopted Restormer-style backbones: (i) the value path remains purely linear, restricting outputs to the span of inputs and weakening expressivity, and (ii) the absence of an explicit global slot prevents attention from encoding degradation context. To address these issues, we propose two minimal, backbone-agnostic primitives: a nonlinear value transform that upgrades attention from a selector to a selector–transformer, and a global spatial token that provides an explicit degradation-aware slot. Together, these additions improve restoration across synthetic, mixed, underwater, and medical benchmarks, with negligible overhead and consistent performance gains. Analyses with foundation model embeddings, spectral statistics, and separability measures further clarify their roles, positioning our study as a step toward rethinking attention primitives for robust all-in-one IR.

Published as a conference paper at ICLR 2026
RETHINKING EXPRESSIVITY AND DEGRADATION- AWARENESS IN ATTENTION FOR ALL-IN-ONE ONE BLIND IMAGE RESTORATION
Bin Ren
Runyi Yang
Ma Xu Zheng
Mengyuan Liu Danda Pani Paudel
Lue Van Gool
Rita Cucchiara'
Nicu Sehe²
Mohimeed University of Artiicial Intelligence
University of Trento
PINSAIT. Solia University SSt. Kliment Ohridski"
"ETH Zunch
"HKUST (GZ) State Key Laberatory of General Artiicial Incellggence. Poking University. Shenzhen Graduate School
"Guangdong Provincial Labantory High Defintion Lnmerssie Media Paking Universit, Sheazhen Groduate School
UUiiversity of Modenn and Reggio Emilii
E1
D1
F2
D2
E2
D
E3
D3
E3
(a]
(b)
E
D
E
D
(c)
(d)
(c) Adverse Weather
(a) WeatherBench
(f) Medical
(1) All-in-One Archs
(II) Average Performance for Benchmarks
09:3
09:3
[a] Input
[b] MOCE
[e] Input
(b) McCE
(a) LQ
(b) AMIR
0933
09:33
Iel ExDA
(d) GT
(c)
[c]
[d) GT (V) Low-Light Enhance
(c)
(III) Deraining
(IV) Denoising
(VI) Medical Imaging
Input
[b]
[a] Input
(b) MoCE
Input
Je) Input (b) Hformer
ENDA (VII) Motion Blur
(c) EXDA
(d) GT
(d)
(VIII) LHS
(VIII)) LHR
(X) WeatherBench
(XI) Ad Weather
Figure I: Architectures:
We propose that the simple encoder-decoder (d) pipeline strong enough
for All-in-One IR. (II) Average Performance: our ExDA consistently produced better results across six challenging All-in-One benchmarks. (III-XI) Qualitative Comparison: Please zoom in for details.
ABSTRACT
All-in-one image restoration (IR) aims to recover high-quality images from di- degradations, which in real-world settings often mixed and unknown. Unlike single-task IR, this problem requires a model to approximate a family of heterogeneous inverse functions, making it fundamentally more challenging and practically important. Although recent focus has shifted toward large multimodal models, their robustness still depends on faithful low-level inputs, and the princi- ples that govern effective restoration remain underexplored. We revisit attention mechanisms through the lens of all-in-one IR and identify two overlooked bottle-
indicates equal contribution; indicates corresponding author: Mengyuan Liu <liumengyuan @ pku.edu.cn>.

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Published as a conference paper at ICLR 2026
necks in widely adopted Restormer-style backbones: (i) the value path remains purely linear, restricting outputs to the span of inputs and weakening expressivity, and (ii) the absence of explicit global slot prevents attention from encoding degradation context. To address these issues, we propose two minimal, backbone- agnostic primitives: a nonlinear value transform that upgrades attention from a selector to a sclector-transtormee, and global spatial token that provides an ex- plicit degradation-awan slot. Together, these additions improve restoration across synthetic, mixed, underwater, and medical benchmarks, with negligible overhead and consistent performance gains. Analyses with foundation model embeddings, spectral statistics, and separability measures further clarify their roles, positioning our study as a step toward rethinking attention primitives for robust all-in-one IR.
INTRODUCTION
Image restoration (i.e., IR) aims recover high-quality images from degraded observations. In real-world scenarios, images often suffer from complex degradations-noiss blur, haze, rain, and their unpredictable mixtures- eachh corresponding to distinct inverse function that must be learned and applied adaptively. This multiplicity of inverse mappings makes all-in-one image restoration fundamentally more challenging than single-task variants: rather than learning one specialized function, the model must approximate family of heterogeneous inverse transformations while remaining robust to mixed and unseen degradations. The stakes for robust low-level vision have never been higher. As computer vision increasingly relies on large multimodal models and foundation architectures, their effectiveness hinges critically on the quality of input images. Corrupted inputs propagate errors throughout the entire pipeline, making reliable image restoration not merely specialized tool, but a foundational prerequisite for robust vision systems.
Recent advances are dominated by Transformer-baseee architectures that skillfully balance global context modeling with local spatial priors. The multi-dconv head transposed attention (MDTA) and gated-dcony feed-forward network (GDFN) introduced in Restormer (Zamir al., 2022) exemplify this trend, replacing computationally prohibitive token-wise self-attention with efficient chamnel-wise attention. This design has become the de-facto standard for high-resolution IR, spawning numerous variants and establishing strong empirical baselines across diverse restoration tasks. However, when viewed through the lens of all-in-one IR, this prevalent design exposes two critical yet overlooked limitations that reflect deeper tension between single-task efficiency and unified IR capability.
First, the attention mechanism's value path remains purely linear: while queries and keys interact through nonlinear softmax operations, values are merely linearly aggregated, constraining outputs to lie within the convex hull of input features. This expressivity bottleneck becomes particularly severe in all-in-one settings, where the model must navigate between vastly different inverse map- pings-from high-frequency noise removal to low-frequency haze correction-yet is constrained by linear combinations of its inputs. Although (Katharopoulos et al., 2020; Shen et al., 2024: Aksenov et al., 2024; Shazeer, 2020) use of nonlincarities on queries and keys enables an efficient linearized softmax, argue that the value space is more critical for learning robust representations. Second, unlike standard ViTs that employ class tokens for global aggregation, channel-wist attention discards the notion of explicit global slots entirely. This forces the degradation context be encoded implicitly across spatial channels, making the model less capable of explicit degradation inference- capability that proves essential when corruption types are unknown and mixed. These limitations matter less in single-task settings where the inverse function is fixed and known, but become fundamental bottlenecks in all-in-one scenarios that demand both expressivity and adaptability.
In this work, we address these bottlenecks through principled rethinking of attention primitives for all-in-one IR. We introduce two minimal, backbone-agnn agnnostic extensions that transform any Restormer- style architecture into more expressive and degradation-awee aware system. Our nonlinear value transform breaks the linear span constraint by augmenting values with lightweight convolutional mappings before aggregation, upgrading attention from a feature selector to a feature selector- transformer. Our global spatial token (GST) restores explicit global context by providing learnable slots that adaptively aggregate Oggradation-relevan statistics and inject them back into the attention computation. These innovations are both theoretically motivated and empirically validated: from function approximation perspective, we show that pre-aggregation nonlinear transformations
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fundamentally expand the realizable function family, while diagnostic analyses involving foundation model embeddings, spectral decompositions, and cluster separability measures demonstrate that global tokens capture meaningful degradation context. Extensive experiments across synthetic, mixed, underwater, and medical benchmarks reveal consistent improvements over strong baselines, with negligible computational overhead and robust transfer across different backhone architectures.
Our contributions address a fundamental gap in understanding attention mechanisms for unified IR:
We identify and theoretically analyze two overlooked bottlenecks in prevalent Restormer- style architectures- value constraints that limit expressivity and absent global slots that limit sngradaatio-awareness
We propose two minimal, backbone-agnoste primitives that address these bottlenecks: nonlinear value transforms that expand the realizable function family, and global spatial tokens that provide explicit degradation context.
We provide theoretical motivation, diagnostic analyses, and extensive empirical validation across diverse benchmarks, demonstrating consistent improvements while maintaining computational efficiency.
RELATED WORK
Image Restoration (IR). IR aims to solve a highly ill-posed problem: reconstructing high-quality images from degraded observations. Owing to its broad importance, IR has been widely applied in numerous applications (Richardson, 1972; Xie et al., 2025; Banham & Katsaggelos, 1997; Li et al., 2023b; Zamfir et al., 2024). Early approaches were dominated by model-based solutions that searched for closed-form results under handcrafted formulations. With the advent of deep learning, learning-based IR methods have rapidly gained popularity. Recent studies include regression-basee
techniques (Lim et al., 2017; Lai et al., 2017; Liang et al., 2021; Chen et al., 2021; Li et al., 2023a; Zhang et al., 2024) and generative pipelines (Gao et al., 2023; Wang et al., 2023b; Luo et al., 2023; Yue et al., 2023; Zhao et al., 2024), built upon variety of architectures such as convolutional networks (Dong et al., 2015; Zhang et al., 2017b;a; Wang et al., 2018), MLP-based designs (Tu et al., 2022), state space models (Guo et al., 2024a; Zhu et al., 2024; Gu & Dao, 2023; Dao & Gu, 2024), and Vision Transformers (ViTs) (Liang et al., 2021; Ren et al., 2023a; Li et al., 2023a; Zamir al., 2022; Dosovitskiy al., 2020: Liu al., 2023). Among them, Restormer (Zamir et al., 2022) introduced a channel-wise attention mechanism (MDTA) to achieve linear complexity while handling high-resolution inputs, and has since become a widely adopted backbone in IR due to its strong balance of efficiency and accuracy. Despite these advances, the majority of IR models are still designed for specific degradation types, such as denoising (Zhang et al., 2017b; 2019). dehazing (Ren et al., 2020; Wu et al., 2021), deraining (Jiang et al., 2020; Ren et al., 2019), and deblurring (Kong et al., 2023; Ren et al., 2023b).
All-in-One Blind Image Restoration. Training separate task-specific models for individual degrada- tions can deliver strong results, yet maintaining one model per task is impractical and environmentally costly. In practice, images are frequently affected by mixtures of degradations, making it unrealistic to handle each corruption independently. This has motivated the study of All-in-On in-nn-n image restoration, where single model is expected to generalize across multiple degradation types (Zamfir et al., 2025; Zeng et al., 2025; Zheng et al., 2024). Different strategies have been explored: AirNet (Li et al., 2022) learns contrastive degradation representations guide reconstruction, whereas IDR (Zhang et al., 2023) formulates degradations as physical components and leverages a meta-learning pipeline. Prompt-based approaches (Potlapalli al., 2024; Wang al., 2023a; Li al., 2023c) push this idea further by conditioning restoration on learned visual prompts, later extended to frequency-aware prompts (Cui et al., 2025) or larger-scale dynamic architectures (Dudhane al., 2024). Although effective, these methods often incur high training costs and reduced efficiency (Cui et al., 2025), which hinders practical deployment. More recently, besides these regression-basee approaches, distribution-orrenteed models also show decent performance while with a larger model size Tian et al. (2025); Luo et al. (2025). In this work, we take different route. Instead of relying on auxiliary prompt modules or complex extra multi-stage strategies, parallel, recent studies have attempted to enhance attention expressivity (Chefer et al., 2021) and incorporate degradation-awar directions motivate a closer examination of the attention operator itself rather than adding heavier auxiliary modules. By examining Restormer-style channel-wise attention, we identify two overlooked
revisit the backbone design itself. In
priors directly into the backbone design (Tang et al., 2025). These
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QK
(4) Sundard
(b) Restomer sole
(c) Openation (Cues)
Figure 2: Comparison of (a) standard ViT attention with CLS in high-level tasks, (b) Restormer-style channel-wise attention in IR, (c) Our proposed attention design that augments values space with nonlinearity and introduces an explicit global slot.
bottlenecks, linear values and the lack of explicit global slot, and propose minimal primitives that directly improve expressivity and provide degradation-awe context for efficient all-in-one Blind IR.
3
THE PROPOSED METHOD
3.1 REVISITING RESTORMER-STYLE ATTENTION: HIDDEN BOTTLENECKS
Transformers have recently become central to low-level vision tasks such as image restoration (IR), where they are often re-designed to balance efficiency and spatial detail recovery. A representative example is Restormer (Zamir et al., 2022), which replaces spatial coken-wise self-attention (Fig. 2a) with channel-wise attention (Fig. 2b) and substitutes the standard MLP feed-forwart layers with gated-dcony feed-forwe forward networks. These design choices make the model computationally feasible for high-resolution inputs and have set strong baseline for deggll-dggadttionn IR.
However, in the broader context of low-level vision, two overlooked limitations emerge. (i), the value (V) path remains purely linear. While queries (Q) and keys (K) interact through nonlinear softmax, values are only linearly aggregated, constraining outputs to lie within the span of the inputs. In the standard ViTs, this limitation is less severe because the MLP-based feed-forward networks provide strong nonlinear transformations. In contrast, IR backbones such as Restormer adopt gated-dconv feed-forward networks, where one branch is essentially linear. As a result, portion of the information bypasses any nonlinear transformation, leaving the overall block with weaker nonlinearity. This makes the expressivity bottleneck of linear values particularly pronounced in all-in-one restoration, where the model must approximate family of diverse inverse functions rather than single degradation. (ii), there no explicit global slot to summarize degradation context. Standard ViTs include a CLS token to aggregate global semantics (Fig, 2a), yet in low-level vision, this token often discarded as "useless" for pixel-level predictions (Liang et al., 2021; Li al., 2023a; Ren et al., 2024). Restormer's s channel-wise attention follows this practice (Fig. 2b), relying instead on local depth-wise convolutions. While sufficient for single-task restoration, this design implicitly assumes that the degradation type is fixed and known. In the all-in-one setting, however, the model must infer degradation type directly from the input. Without dedicated global slot, there is explicit mechanism to encode degradation statistics into the representation. What may appear redundant in single-task IR thus becomes indispensable in all-in-one IR, where such a slot can naturally evolve into degradation embedding, capturing global statisties not only across channels but also explicitly across spatial structure (Fig. 2b). These two limitations motivate our subsequent exploration of nonlinear values and explicit global tokens.
3.2 NONLINEARITY MATTERS IN VALUES FOR EXPRESSIVITY
Building on the identified bottleneck in Sec. 3. empirically validate the critical role of nonlinear values in all-in-one restoration. The linear value constraint severely limits the model's ability to approximate diverse inverse mappings required across different degradations.
To isolate this effect, we design experiments with restoration complexity. Our syuthetic function combines nonlinear sensor response, blur kernels, addi- tive/multiplicative noise, and quantization effects. Our MNIST restoration applies realistic corruptions including nonlinear response curves, motion blur, spatially-varyine noise, and compression artifacts. Both scenarios require modeling multiple degradation characteristics simultaneously, analogous to all-in-one restoration challenges.
degradations that mirror all-in-one
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Algorithm 1 GST: Content-Adaptive Global Spatial Token Generation
Require: Input features X E IR.BxxxxxW heads ht tokens per head K, stride 8
Ensure:
Degradation-awarr global tokens G E TR x
1: Efficient Spatial Compression: X = AvgPool, (X) B
2: Multi-Head Token Projection: + proj X RRBNKKH,,W.
3: Content-Adaptive Spatial Attention:
4: Commoet Soatia (Reshape @ [B, h K, -1)))
5: Resolution Recovery: G BilinearUppamplle (Geumpuci, size = (H, W))
6: return G E Bx
> Ready for attention concatenation
Fig. 3 reveals consistent patterns across both settings. In the synthetic function approximation setting (a), linear value attention systematically fails in critical regions (or- ange highlights), achieving 50.4% worse convergence encounters inherent expressivity limitations. MNIST restoration (b) confirms this realistic image data, where nonlinear values achieve 5.92 dB PSNR improve- ment (19.2+25.11 dB) and produce visually superior reconstructions compared to blurry, artifact-laden out- puts from linear value attention.
Fracion sprressaiis conregaez conlacar
5
5
5
5
These findings confirm our analysis: in Restormer-style architectures where gated FFN allows information to bypass nonlinear transformations, the linear value con- straint becomes critical expressivity bottleneck for all-in-one restoration requiring diverse degradation mod- cling. This motivates enhancing value expressivity through V' = VV),), where fe introduces nonlinear- ity beyond the linear span of inputs. We adopt a residual formulation to balance preservation and transformation:
3 3
MIIST relince
Figure 3: Nonlinear value transforms out- perform linear value attention in both (a) function approximation and (b) MNIST restoration, achieving better convergence, higher PSNR/SSIM, and improved quality.
V + go
99 = Convix1 + DWConvaxs + GELU + ConVix1
Two insights guide this design. First, pre-aggregation placement is essential since attention Softmax(QK") /VVVV' constrains outputs to linear combinations cannot escape this fundamental limitation. Second, the lightweight de provides sufficient expressivity for diverse degradation modeling while maintaining efficiency. This transforms channel-wise atten- tion from a linear feature selector into a nonlinear feature transformer, fundamentally addressing the expressivity gap between single-task and all-in-one restoration requirements.
combinations-post--ggreeee post-aggregation nonlinearity
3.3 GLOBAL CONTEXT AND THE MISSING SLOT
Building on the analysis in Sec. 3.1, we now address the second identified limitation: the absence of explicit global context mechanisms in Restormer-style architectures. While Sec. 3.2 tackled the expressivity bottleneck through nonlinear value transforms, the degradation inference challenge requires fundamentally different approach, introducing explicit degradation-awart slots that can capture global spatial statistics. The core issue is that without dedicated global tokens, degradation context can only be distributed implicitly across local channel interactions or captured through cross-channel attention mechanisms. This becomes particularly problematic in all-in-one settings where the model must distinguish between fundamentally different corruption types. These global spatial signatures are best captured through explicit global analysis rather than relying solely on local depth-wise convolutions or implicit cross-channel attention.
We address this through Global Spatial Tokens (GST), which provide explicit degradation-aware slots while maintaining computational efficiency. As detailed in Alg. 1, our design generates content- adaptive spatial attention maps through stride-s spatial compression and learaale projection. The key innovation lies in content-ade content-adaptive pooling: rather than fixed global averaging, GST learns spatial attention maps through learnable projections followed by spatial softmax normalization, enabling each
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Table Comparison to state-of-the-art on three degradations. PSNR (dB, 1) and SSIM (1) metries are reported on the full RGB images. Best performances is highlighted. means unreported results.
Dehazing
Deraining
Denoising
Method
Venue.
Params.
Average
SOTS
Raint00L
BSD68
BSD68
BSD68
BRDNet (Tion et al., 2020)
NN'20
23.23
895
27.42
.955
32.26
898
29.76
836
26.34 34
693
27.80
.443
LPNet (Gao et al., 2019)
CVPR 19
20.84
.888
24.88
.784
26.47
.778
24.77
.748
21.26
.552
23.64
.738
FDGAN (Dong
2020)
AAA1220
24.71
.929
29.89
.333
30.25
.100
28.81
868
26.43
.776
28.02
8833
(Fan et al., 2019)
1FAMI'9
2M
26.92
931
32.62
931
33.05
914
30.41
861
26.90
.740
29.98
886
MPRNet (Zamir et al., 2021)
CVPR'21
16M
.955
33.57
954
33.54
30.89
27.56
.779
30.17
.899
AirNet (Li et al., 2022)
CVPR'22
9M
27.94
962
34.90
967
33.92
933
31.26
888
28,00
.797
31.20
.910
NDR (Yao al., 2024)
28M
25.01
860
28.62
848
28.72
27.88
.798
720
25.01
.810
PromptIR (Potlapalli al., 2024)
22
36M
30.58
974
36.37
972
33.98
933
31.31
888
28.06
.799
32.06
.913
McCE-IR-S (Zamfic et al., 2025)
CVPR225
11M
30.98
.979
38.22
983
34,08
.93
31.42
888
28.16
.798
32.57
.916
AdaIR (Cui al., 2025)
ICLR'25
29M
31,06
980
38.64
983
34.12
935
31.45
892
28. 19
.012
32.69
.918
MoCE-IR (Zamfir et al., 2025)
CVPR/25
25M
31.34
979
38.57
9984
34.11
932
31.45
888
28.18
800
32.73
.917
ExDA (Own))
ICLR'26
22M
31.58
.982
39.13
998
34.32
.937
31.56
.895
28.32
.808
32.96
.921
Methods with the assistance vision language, multi-task learning. natural language prompts, multi-modal control
DA-CLIP (Lao al,, 2024)
ICLR'24
125M
29.46
963
36,28
968
30.02
821
24.86
22.29
.476
Alt 2 z. (Wu et al., 2024)
ACM 24
36M
30.83
.979
37.94
.922
34.06
.944
31.42
881
28.14
801
32.49
.917
I (Conde et al., 2024)
ECCV"24
16M
30.22
959
37.98
978
34.15
933
31.52
890
28.30
804
32.43
9933
UniProcessor (Duan et al., 2025)
BCCV'24
1002M
31.66
.999
38.17
.92
34.08
31.42
28.17
803
32.70
.988
YeU-Net (Zeng et al., 2025)
CVPR'25
35M
30.71
980
38.93
984
34.13
935
31.48
892
28.23
804
32.70
.919
Perceive (Zhang al., 2025)
975
38.29
980
34.13
31.53
890
32.63
.917
DEPIR (Tian et al,, 2025)
CVPR'25
31M
31.87
980
38.65
982
34.12
938
31.47
893
28.25
806
32.88
.919
DA-RCOT (Tang et ol., 2025)
TPAMI'25
50M
31.26
977
38.36
983
33,98
.94
31.35
.800
28.10
801
32.60
.917
token to naturally develop distinct spatial emphasis patterns that correspond to different degradation characteristics. This enables natural specialization during training- noise tokens activate scattered high-frequency regions, blur tokens emphasize smooth low-frequency areas, and haze tokens respond to scage illumination structures, all without explicit supervision or degradation labels.
The generated GST tokens G R Bxhx KXH are seamlessly integrated into channel- attention through direct concatenation with query, key, and value representations:
=Q @ G, V + G] [xxx((++K)*HW
(2)
where @ denotes channel-wise concatenation and V' is the nonlinear-enhanced value from Sec. 3.2. After standard attention computation Attn Softmax K*T /r)V", V*' we separate local channel features and global token contributions, re-injecting GST influence via learnable residual scaling:
Output p..c.. + a At..).
where a is initialized to 0.1 for gradual learning without overwhelming local features.
This design transforms the discarded CLS token concept into a valuable primitive for all-in-ona scenarios. By providing explicit degradation-aware slots that naturally specialize during training, GST enables coherent global strategies while maintaining channel-wise attention efficiency. The content- adaptive nature allows each attention head to learn specialized global patterns without requiring explicit degradation labels or supervisory signals.
4 EXPERIMENTS
We evaluate ExDA under six All-in-One IR benchmarks, i.e., (i) All-in-One (3Degradations), (ii) All-in-Ol (SDegradations), (ii) Mixed Degradation, (iv) Adverse Weather Removal, (v) Real-world WeatherBench, and (vi) Medical All-in-One The macro architecture of our model, the datasets, and im- plementation details are provided in our appendix. Project pagechitss//mmmmzngenggittubbbie http://nggggggggtuu..o///./a../a
4.1 MAIN RESULTS.
3- 3-Degradation Setting. The results in Tab. 1 show that ExDA consistently outperforms other methods, even outperforming those with the assistance of language, multi-task, or text/visual prompts. Notably, 0.23 dB higher than the recent MOCE-IR on PSNR, while with 3M fewer parameters.
5-Degradation Setting. Building on the previous setting, we further include deblurring and low-light enhancement two additional degradation types (Li et al., 2022; Zhang et al., 2023). As shown in

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Published as a conference paper at ICLR 2026
Table 2: Comparison to state-of-the-art on five degradations. PSNR (dB, 1) and SSIM (1) metries are reported on the full RGB images with (*) denoting general image restorers, others are specialized all-in-one approaches. Best performance is highlighted.
Dehazing
Deraining
Demoising
Deblurring
Low-Tight
Method
Venue
Params.
Average
SOTS
Rain 10OL.
BSD68...2
GoPro
LOLvl
NAFNet" (Chen et al., 2022n)
22
17M
25.23
939
35 56
967 31,02
883
26.53
808
20:49
809
27.
76
881
DGUNee (Mou
2022)
22PR'22
17M
24.78
990
27.25
21.87
28.32
.891
SwinIR" (Liang et al., 2021)
ICCVW'21
IM
21.50
.91
30.78
923
30.59
868
24.52
773
17.81
773
25.04
.835
Restormer (Zamiz et al., 2023)
CVPR222
26M
24.09
.927
34.81
.62
31.49
.844
27.22
889
20.41
8866
27.60
.881
MambalR" (Cao al., 2024g)
2CCV'24
27M
25.81
.944
36.55
971
31.41
884
28.61
.875
22.49
852
28.97
.901
(Fan et al., 2019)
1
2M
20.54
.826
21
23.09
19.86
19.83
21.05
.743
weatswellber
CVPR'22
38M
21.32
.885
29.43
.005
29.00
.841
25.12
.777
21.21
.792
25,22
.836
TAPE (1.u et al., 2022)
ECCV'22
1M
22.16
.86
29.67
904
30.18
855
24.47
.763
18.97
.621
25.09
.801
AirNet (Li et 2022)
2022)
C2
9M
21.04
884
32,98
951
30,91
882
24.35
788
18.18
735
25.49
.847
IDR (Zhang al., 2023)
CVPR'23
15M
25.24
.94
35.63
965
31.60
867
27.87
846
21.34
826
28. 28.34
.893
PromptI (Podapalli et, 2024)
N2
36M
30.41
.972
36.17
970
31,20
885
27.93
22.89
29.72
.901
McCE: IR S (Zamfir et al., 2025)
CVPR'25
11M
31,33
.978
37.21
978
31,25
.844
28.90
.877
21 21.68
851
30,08
.913
AdaIR (Cui et al., 2025)
ICLR'25
29M
30.53
.78
38.02
981
31.35
889
28. 12
858
23.00
845
30.20
.910
McCE-IR (Zamfir et al., 2025)
C5
25M
30.48
974
38
31.34
887
30,05
.899
23.00
852
30.58
.919
ExDA (Ours)
ICLR'26
22M
31.14
.981 39.23
.985
31.52
.893
29.06
.878
23.19
.859
30.83
.919
Methods with the assistance of natural language prompts or multi task learning
InstructRR 5D (Conde al., 2024)
ECCV'24
16M
36.84
.93
27.10
956
31.40
.887
29.40
.886
23,00
833
29.55
.908
Art R (Wu et al., 2024)
ACM MM 24
36M
29.93
908
22.09
891
29.43
843
25.61
776
21 99
811
25.81
.846
VLU Net iZeng el al., 2025)
CVPR'25
35M
31.43
22.29
30.11
.955
Perceive.IR IR (Zhing al., 2025)
TIP'25
42M
28.19
964
37.25
977
31.44
887
29.46
886
22.88
833
29.84
.099
DEPIR (Tian et al., 2025)
CVPR25
31M
31.64
.79
37.62
978
31.29
889
28.82
.87
23.82
883
30.64
.913
DA-RCO (Tang et al., 2025)
TPAMI'2
50M
30.96
.95
37.87
980
31,23
888
28.68
.87
23.25
836
30.40
.911
Table 3: Comparison on composited degradations. PSNR (dB, 1) and SSIM (t) are reported.
CDDIF-Singie
CDDII-Deable
CDDII
Method
Parsms
Low (T.
Hore (TT) Rain (R) Snow (8)
L+H1
L+R
L+S
11+R
I+SS
L+I++R
L-H+-
AirNet
9M
24.83
7778
24:21 991 26.55 .89] 26.79 .919 23:23 .779
22,82
710 23.29 .723
22.21
868
23.29 901 21.90
708 22.24 .25
23.75 8144
Promqti
26.32
805
369
946
960
.789
771
.661
924
752
747
850
WGWSNet
24.39
7774 27.00
981
33:15
964
34,83
9773
25.27 800
772
766
27.25
955
27.65 960
712 23.97
271
363
Weaths
83M
23.58
763 21.99
904 24.85
885
24.80
888 21.83
75%
22,60 730
22.12
707
21
868
21 99 868
21.23
716 21 .84 698 22.49 779
Onekestore
6M
26.48
826 32.52 950
33.40
964
94,31 .973 25.79
822 25.58
7799 25.19.
7889
29.99
957
30.21
964
24.78
788
24.90
791 28.47 8878
MoCBsIR
27:36
.824
.817
.800
.793
9964
9970
.789
790
.881
ExDA (Chrs)
22M
27.60
.837 34.44 .91 35,33 ,973 36,85 (981 26.95 .834 26.80 .817 26.73
.809 31.17 .968 31.40 .972 25.59 .801 25,86 (799 29.97 .891
Table 4: Comparisons for 4-task adverse weather removal. Missing values are denoted by '-'.
Method
Venue
Snow 100K-S
Snow 100-L
Outdoor Rain
RainDrop
Average
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
One (Li al., 2020)
CVPR'20
28:33
.382
24.71
.898
21.12
.927
TrnsWeather (Valetamen ol., 20224)
CVPR'22
$2.51
.934
29.
888
28.83
.500
30.17
.916
30.20
909
Chen (Chen 2022b)
CVPR'22
34.42
.947
30.23
.907
29.27
.915
31.81
.931
31.43
925
WGWSNet
CVPR'23 23
34.31
30. 16
901
32.38
WeathetDilfes (Oodenizci& & Legenstein, 2023)
TPAMU'23
35.83
.957
30.09
S04
29.64
.91
30.71
.931
31.57
.911
WeatherDafygs (Undenizei & Legenstein, 2013)
TFAMI/25
35.02
.952
29.58
.344
29.72
.922
29.66
993
31.00
923
AWRCP
2023)
ICCV23
36.92
31:92
914
31.39
33.04
Godkormer (Wang et al. 2024)
DCY'24
37.46
964
$1.71
923
31.87
.953
32.39
.936
MPereeiver (Ai et al, 2004)
CVPR'24
36.23
957
$1.02
916
31.25
925
33.21
.29
32:03
932
DTPM (Ye al., 2024)
CVPR/21
37.01
.966
30.92
.917
30.99
.34
32.72
.944
32.91
.40
Ilistfoomer (Sun 2024)
ECCV'24
37.41
32.08
FADA (Ours)
IC
37.97
.968
32.49
.930
32.70
.945
32.66
.944
33.92
Tab. 2, our method achieves the highest average PSNR, surpassing even substantially larger models, as well as those leveraging additional modalities or pretraining. Moreover, for dehazing and deblurring, our approach also delivers competitive results compared to existing methods.
Composited Degradation. To better capture real-world complexities, we extend OneRestore (Guo et al., 2024b) to cover eleven scenarios, including rain, haze, snow, low-light conditions, and their composite variants. As summarized in Tab. 3. our approach consistently surpasses state-of-the-art methods such as AirNet (Li et al., 2022), PromptIR (Potlapalli et al., 2024), WGWSNet (Zhu et al., 2023), WeatherDiff (Özdenizei & Legenstein, 2023), OneRestore (Guo et al., 2024b), and MoCE- IR (Zamfir et al., 2025). Notably, our method achieves a 0.92 dB PSNR improvement over MOCE-IR, underscoring its strength in addressing complex, mixed degradations.
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Published as a conference paper at ICLR 2026
Table 5: Comparisons for Real-Worid WeatherBench (Guan et al., 2025).
Method
Venue
Dehaze
Durain
Desnow
Averago
PSNR
SSIM
PSNR
SSIM
PSNR
SSLM
PSNR
SSIM
Resturmer (Zamir et 2022)
C2PR'22
19.30
.687
34.48
.955
27.95
8.6
27.25
.823
AutSe: al,, 2022)
CVPR 22
20.94
.705
33.59
22,05
25.35
.809
Tram:Wather (Vahaamms
CVPR'23
24.96
.793
PrompitK (Pociapalli et ol., 2024)
NeuriPS 25
21.11
.713
$4.53
994
27.95
336
27.86
881
WoWS-NNr (7hi 2025)
CVPR
13.79
.613
37.08
.61
20.81
009
23,89
781
Histofommer
BOCV"24
25.30
778
AddR (Cui et 2025)
ICLR'24
23.08
.731
34.87
28.44
837
838
(Ous)
ICT.R'
.739
35,86
Table 6: Comparisons for 3-task Medical Image Restoration. Missing values are denoted by '-1,
Method
Venue
MRL-Super- Resolution
CT-Denoising
PET-Syatheiis
Average
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Resturmer (Zamir et al., 2022)
CVPR'22
31.72
906
33.61
918
37.13
.47
34,16
934
AirNet (1i a al., 2022)
CVPR 22
31.39
931
33.62
917
37.17
945
34,06
931
Spach Transfomer (Jang 2023)
31.18
33.90
930
DRMC (Ying al., 2025)
MICCAI'23
29.55
903
33.28
915
36,19
945
33.00
930
Hfoemer (Luthra
MadIA
29.19
.333
32.44
908
35.11
.09
32.25
397
(Yang ol., 2024)
MICCAU124
938
33.68
34.28
934
EXIM (Ours)
ICLK'26
31,95
.938
33.67
.918
37.27
.947
34.30
.934
Adverse Weather Removal. Following prior works (Valanarasu al., 2022b; Zhu et al., 2023), we evaluate ExDA on three challenging deweathering tasks: snow removal, rain streak and fog removal, and raindrop removal. Tab. 4 shows that ExDA consistently outperforms existing SOTA approaches across nearly all datasets, with the only exception being the PSNR result on RainDrop. These substantial improvements across diverse degradations highlight the robustness of ExDA. Especially, our method achieves notable 0.24 dB PSNR gain over Histoformer (Sun et al., 2024).
Real-world WeatherBench. We further validate our approach on the challenging real-world Weath- erBench benchmark (Guan et al., 2025), which involves diverse weather degradations such as haze, rain, and snow. As reported in Tab. 5, our method achieves the best overall performance, reaching an average PSNR/SSIM of 29.68/0.851 Notably, it delivers the highest scores both dehazing and desnowing, and achieves competitive results deraining compared with the strongest prior models. These consistent improvements across multiple degradations highlight the robustness and generalization ability of our method in handling complex, real-world weather conditions.
Medical All-in-One. Finally, evaluate our method medical image restoration tasks using the AMIR dataset (Yang et al., 2024), which unifies three restoration tasks within a single model. The results in Tab. demonstrate the strong cross-domain applicability of our method, showing large gains in MRI super-resolution and competitive performance on the others.
Visual Results.
Fig. 1 & shows that our method restores high-quality and faithful results across diverse all-in-one settings, producing clearer boundaries and more accurate details than others.
4.2 ABLATION STUDY.
Component Analysis & Model Scaling Exploration. We start with two PromptlR- based baselines (with and without prompts). Unlike these settings, our framework does not depend on extra prompts for strong performance. Instead, the key improvements come from the proposed nonlinear value design and GST module. As shown in Fig. 5, adding nonlinear value ("b Nonlinear Value") provides a clear gain, and further integrating GST ("b + GST"" yields consistent improvements. When combined ("c + d'"), our full model achieves the best performance (32.96/0.921) confirming the effectiveness of these components. We then examine model scaling. Both the Small and Tiny versions ("e" and "f") remain highly competitive despite their reduced size, with the Tiny variant (only 6M parameters) still approaching the full model's performance. This demonstrates that once the core components well designed, even extremely lightweight models deliver strong results, making our framework both effective and scalable. The below ablations are all based on tiny model.
Nonlinear Value Design.
To systematically investigate how nonlinear value transforma- tions should be incorporated, we conduct ablations along three dimensions. First, we compare two formulations: an in-place nonlinear mapping (V' (V)) versus a resid- ual form (V" = V (V)). Results consistently favor the residual design, which preserves the original representation while enriching it with nonlinear transformations.
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Published as a conference paper at ICLR 2026
Degended hopt
Airiet
PrometiR
Mott-If
D:DA (Duns)
Nain
Nokee (233031
McCE-R
ExDA [Ous]
GT
McCE-IR
ExDA [Ours]
GT
Lowinghtt
Degrided Input
WGWShett
WeatherGif
HistoFormer
EvOA (Our)
AMIR
1OA [Oum)
HOST
AVR
D:DA (Dug)
HELGT
Medical
Figure 4: Visual results under diverse degradations and domains, spanning rain and noise (3-deg), lig-tight (5-deg), LHS composite Now-lightthaze+snow)) raindrop (adverse weather), and medical imaging. Please zoom in for details.
POSK
20.91
0,923
32.03 9.009
13.04
0.814
PS (00)
30.4
Pringii Foinet
Figure 5: Qualitative results on medical restoration.
Second, we examine the choice of go(). While non-parametric nonlinearities (e.g., pure Sigmoid or GELU) provide limited gains, learnable param- eterized mappings achieve substantially better per- formance, confirming the need for adaptive flex- ibility. Third, analyze where nonlinear values matter most in all-in-one blind IR by applying them selectively both. The evidence shows that deploying them in both the encoder and decoder yields the strongest improvements. Overall, these findings establish that residual, learnable, and fully integrated nonlinear value designs form the most effective strategy, significantly enhancing expressivity for diverse degradations in all-in-one IR (Tab. 7).
Table 7: Ablations on Nonlinear Value Design
Design Choice
Results
(IB, t)
SSIM (f)
In-place Noulinear (Signoid)
32,30
Residual Nonlincar (Sigmoid)
32.63
916
In-place Norlinear (GELU)
32.45
.914
Residual Noulincur (GELU)
32.71
.919
the encoder, the decoder,
Non-parametric (GELU)
32,30
914
Parametric (Learnale))
32.71
.919
Encoder-only
32.65
916
Decoder-only
32.60
.915
Encoder Decoder
32.71
.19
GST Configuration. We analyze the effect of the stride s in GST, which controls the granularity of spatial information compression. When
1, the model preserves excessive redundancy and achieves 32.45 dB PSNR. Larger strides, such as s = 4 and s = 8, oversimplify spatial structures and reduce performance 32.52 and 32.40 dB, respectively. The best result is obtained with s = 2, reaching 32.71 dB PSNR, striking the optimal balance between information preservation and compactness. Therefore, adopt 2 as the default configuration in method.

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Published as a conference paper at ICLR 2026
0.12
33.09
30
80
600
030
1000
360
12.5
35.5
2840
22.
35
125
260
225
Figure 6: Efficiency trade-offs of ExDA. (a) Resolution-latena scaling (ExDA-Tiny) (b) Model-
size-latency. (c) Model-size-qualit (PSNR). ExDA-S provides the best accuracy-cfficieney balance.
SNE (b) G81)
Euth G8T)
Figure 7: t-SNE and UMAP visualizations before (a,c) and after (b.d) applying GST.
Figure GST-driven attention visualization across 5 degradations and 1 mixed case. GST consistently highlights degradation-relevant regions, indicating that it captures meaningful degradation cues.
Efficiency Analysis. Fig. 6 summarizes the engineering trade-off's of ExDA--Tiy/Small/BBa Latency grows smoothly with model size 2209.6-385.2 ms), while restoration quality increases correspondingly (32.71-32.99 dB). ExDA-Small provides the best balance. To illustrate scalability with input resolution, we sweep ExDA-Tiny from 256² to 10242, observing mear-linear O(H)) growth (54.5 -840.1 ms). This confirms that ExDA scales predictably to high-resolution inputs.
Degradation-Awarr Representation. As shown in Fig. 7, GST noticeably improves the organization of the embedding space: without GST, degradation types tend to overlap, while GST produces clearer and more compact clusters. To ensure fair comparison given the uneven dataset sizes, we sampled 68 images for noise, blur, haze, and rain, and used all 15 samples for low-light. This balanced setting leads to substantial improvements in NMI (0.71=0..88) and ART (0.56-00.8) reflecting stronger serraaatioo-awwarenes
Fig. 8 further illustrates this effect by visualizing GST attention across blur, haze, noise, rain, low-light, and mixed case (Low + Haze Snow). Across all settings, GST consistently attends to eegraddationrelevaane regions such as blur boundaries, dense haze areas, noise-heavy textures, rain streaks, dark low-light structures, and mixed artifacts- shlfactt-sshowin that the module captures interpretable global cues that align with the improved embedding separation.
5 CONCLUSION
In this work, we revisited attention mechanisms for all all-in-one image restoration and uncovered two central limitations in Restormer-style architectures: the linear constraint on value aggregation and the lack of explicit global context modeling. To address these issues, we introduced nonlinear value transform and global spatial token, two minimal yet powerful primitives that are both backbone- agnostic and lightweight. Our design consistently improves restoration quality across synthetic, real-world, underwater, and medical benchmarks, while remaining effective even in compact models with only a few million parameters. Beyond empirical gains, our analyses clarify why nonlinear value transformations enhance expressivity and why explicit global slots enable degradation degradattion-avareness. awareness. These insights highlight a broader principle: rethinking the role of values and global context is key to building robust, efficient, and general-purpose restoration models.
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ACKNOWLEDGMENTS
This work was partially supported by the National Natural Science Foundation of China (No. 62473007), the Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Me- dia Technology (No. 202481212010066, the Shenzhen Innovation Science and Technology Foundation for the Excellent Youth Scholars (No. RCYX202312 1090248064), and the FIS project GUIDANCE (Debugging Computer Vision Models via Controlled Cross-modal Generation) (No. FIS2023-03251).
ETHICS STATEMENT
This work studies fundamental methods for all-in-one image restoration. Our research focuses on algorithmic improvements to attention mechanisms and does involve the collection annotation of new data. All datasets used in experiments (including synthetic, mixed, underwater, and medical benchmarks) are publicly available, widely used in the community, and employed strictly according to their licenses. The proposed methods are designed to improve robustness and generalization of image restoration models and do not raise foreseeable ethical concerns beyond the general considerations of responsible AI research.
REPRODUCIBILITY STATEMENT
We emphasize reproducibility throughout this work. Detailed descriptions the architecture, training setups, and evaluation protocols provided in the main text (see corresponding sections) and in the Appendix. If the paper is accepted, we will release all source code, pretrained weights, and configuration files immediately facilitate transparent verification and future research.
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A EXPERIMENTAL PROTOCOLS
A.1 DATASETS
3 Degradation Datasets. For both the All-in-One and single-task settings, we follow the evaluation protocols established in prior works (Li et al., 2022; Potlapalli et al., 2024; Zamfir et al., 2025), utilizing the following datasets: For image denoising in the single-task setting, we combine the BSD400 (Arbelaez al., 2010) and WED (Ma al., 2016) datasets, and corrupt the images with Gaussian noise at levels a E {15, 25, 50). BSD400 contains 400 training images. while WED includes 4,744 images. We evaluate the denoising performance on BSD68 (Martin al., 2001) and Urban 100 (Huang et al., 2015). For singlc-task deraining, we use Rain (Yang et al., 2020), which provides 200 clean/rainy image pairs for training and 100 pairs for testing. For single-task dehazing, we adopt the SOTS dataset (Li et al., 2018 1), consisting of 13, training images and 500 testing images. Under the All-in-One setting, we train a unified model on the combined set of the aforementioned training datasets for 120 epochs and directly test it across all three restoration tasks.
5 Degradation Datasets. The 5-degradation setting built upon the 3-degradation setting, with two additional tasks included: deblurring and low-light enhancement. For deblurring, we adopt the GoPro dataset (Nah et al., 2017), which contains 2,103 training images and 1,111 testing images. For low-light enhancement, images and 15 testing images. Note that for the denoising task under the 5-degradation setting, we report results using Gaussian noise with 25. The training takes 130 epochs.
the LOL-v1 dataset (Wei et al., 2018), consisting of 485 training
Composited Degradation Datasets. Regarding the composite degradation setting, we use the CDD11 I dataset (Guo et al., 2024b). CDD11 consists of 1,183 training images for: (i) 4 kinds of

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single-degradation types: haze (H), low-light (L), rain (R), and snow (S); (ii 5 kinds of double- degradation types: low-light + haze (l+h). lighttgttrain (L+R), low-light + snow (L+S), haze + rain (H+R), and haze + snow (H+S). (iii) 2 kinds of Triple-dege dippeddegradttion rype: low-light haze rain (L+H+R), and low-light + haze + snow (L+H++S) We train our method for 150 epochs (fewer than 200 epochs than MoCE-IR (Zamfir et al., 2025)), and we keep all other settings unchanged.
Adverse Weather Removal Datasets. For the deweathering tasks, we follow the experimental setups used in Trans Weather (Valanarasu et al., 2022a) and WGWSNet (Zhu et al., 2023), evaluating the performance of our approach on multiple synthetic datasets. We assess the capability of ExDA across three challenging tasks: snow removal, rain streak and fog removal, and raindrop removal. The training set, referred to as "AllWeather". is composed of images from the Snow100K (Liu et al., 2018), Raindrop (Qian et al., 2018), and Outdoor-Rain (L.i et al., 2019) datasets. For testing, we evaluate our model on the following subsets: Snow 100K-S (16,611 images), Snow100K-L, (16,801 images), Outdoor-Ret Rain (750 images), and Raindrop (249 images). Same as Histoformer (Sun et al., 2024), we train ExDA on "AllWeather" with 300,000 iterations.
Real- WorldWorld WeatherBench, For the real-world WeatherBench, we adopt newly released benchmark from Guan et al. (2025), which contains in total 41,402 training pairs across 3 kinds of degradations (i.e., rain, haze, and snow). There are 600 pairs for testing and 200 pairs per degradation. Under the All-in-One setting, we train unified model on the combined set of the datasets for 120 epochs, and directly test it across all three restoration tasks
Medical All-in-One Dataset. AMIR dataset (Yang et al., 2024) include three important medical image restoration tasks, include (i) MRI super-resolution dataset from public LXI MRI benchmark. We use the public IXT MRI dataset, containing 578 high-quality T2-weighted MRI volumes. Low- quality images are generated by 4x k-space downsampling (retaining 6.25% % of central data). The dataset is split 405/59/114 14 for craining/validaaiooneesst Mayo Clinic Low-Dose CT Grand Challenge, These images originate from 10 patients. with 8 allocated for training, for validation, and 1 for testing purposes, after slicing we get 1/53//128/211 for wraining/validation/esssnn (iii) PET Synthesis include 159 HQ PET images acquired by ( (Yang et al., 2024). The 3D volumes have dimensions of 192x192x400 Each volume is divided into 192 axial slices (1 (192x400), excluding slices containing only air. Patient data is split into 120 training, 10 validation, and 29 testing cases.
(ii) CT Denoising dataset from NIH AAPM-
A.2 IMPLEMENTATION DETAILS
Implementation Details. Our ExDA framework is designed to be end-to-end trainable, removing the need for multi-stage optimization of individual components. The architecture adopts robust levell encoder-decoder structure, with varying number of Mixed Degradation Attention Blocks (MDAB) at each level--pecifically [3, 5, 5, 7] from highest to lowest resolution in the Tiny variant. Following prior works (Potlapalli et al., 2024; Zamfir al., 2025), we train the model with batch size of 32 in both the 3-Degradation All-in-One Onee and single-task settings. The optimization uses combination of L1 and Fourier loss, optimized with Adam (Kingma & Ba. 2015) (initial learning rate of 10 4 31 = 0.9, 32 = 0.999) and a cosine decay schedule. During training, we apply random cropping to 128x 128 patches, along with horizontal and vertical flipping as data augmentation. All experiments are conducted on single NVIDIA H200 GPU (140 GB). Memory usage is approximately 42 GB for the Tiny (i.e., EXDA- -T) model and 56 GB for the Small model (i.e., EXDA -S).
A.3 OPTIMIZATION OBJECTIVES
The overall optimization objective of our approach is defined as:
(4) Here, LFourier denotes the real-valued Fourier loss computed between the restored image and the ground-truth image, and LseD represents our proposed contrastive learning objective in the SPD (Symmetric Positive Definite) space.
Liotal L1 + Afre X LFourier
Specifically, we adopt an & loss that adopted in IR tasks (Potlapalli et al., 2024: Zamfir et al., 2025; Li et al., 2022; Ren et al., 2026; Cui et al., 2025; Ren et al., 2024: Li et al., 2025), defined as C = ( 2/1, to enforce pixel-wise similarity between the restored image 2 and the ground- truth image r. Lrourier, utilized in MOCE-IR (Zamfir al., 2025; Cui et al., 2025), enhance
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Bedie Lits
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Figure A: ExDA Model Architecture Framework. (a) Demonstrates the overall pipeline for ExDA, (b) details the ExDA block design.
Table A: The details our the tiny and small version of our ExDA. FLOPs are computed on an image of size 224 224 using a NVIDIA Tesla A100 (40G) GPU.
ExDA Base
ExDA
E&DA T
The Number of the MDAB crisses 4 scales The Input Embedding Dimension
[4, 64,6,6,8
[4, 6.6,
[3, 5,5, 7] 26
48
32
The FFN Expansion Factor
2
2
The Number of the Refinement Blocks
3
3
Params. (L) FLOPs (L)
22.42M
10.05M 47
5.83 M 27
109G
drequency-domain consistency, the real-valued Fourier loss, is defined as:
C = Freal (f) - FFea(())]l + +||Fimag - -=)aa(z)ll
(5)
where á and denote the restored and ground-truth images, respectively. Freal.)) and Finay (-) represent the real and imaginary parts of the 2D real-input FFT (i.e., rfft2). The final loss is computed as the é distance between the real and imaginary components of the predicted and target frequency spectra. Same as MOCE-IR (Zamfir et al., 2025), Afre is set to 0.1 throughout our experiments.
B MACRO ARCHITECTURE INTRODUCTION
The overall architecture of ExDA is illustrated in Fig. A. At a maero level, it adopts a U-shaped network with four hierarchical levels. Initially, a convolutional layer extracts shallow features from the degraded input, creating patch embedding of size H W C. As in standard U-Nets, each encoder stage doubles the embedding dimension and halves the spatial resolution, with skip connections transferring information to the corresponding decoder stage. In the decoder, features are merged with the previous decoding stage via linear projection. Finally, global skip connection links input to output, preserving high-frequency details and producing the restored image.
Unlike most recent approaches that rely on additional prompts or large language models (LLMs), we argue that a simple encoder-decoder architecture, as illustrated Fig. (()(d), is sufficient to tackle the challenges of all-in-one image restoration. The key lies in identifying and addressing the latent bottlenecks.
Model Scaling, We propose two scaled variants of our ExDA, namely Tiny (ExDA -T) and Small (ExDA -S). As detailed in Tab. A, these variants differ in terms of the number MDAB blocks across scales, the input embedding dimension, the FFN expansion factor, and the number of refinement blocks.
Efficiency Comparison. Tab. A presents detailed comparison of PSNR, memory usage, parameter count, and FLOPs. Our Tiny model (ExDA -T) achieves the best deffciency-performan trade-off: with only 6.21M parameters and 16G FL.OPs, it outperforms all prior methods, including larger
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models like PromptIR (Potlapalli al., 2024) and MoCE-IR-S (Zamfir et al., 2025). Notably, ExDA surpasses MoCE-IR-S by +0.26 dB while requiring less than half the computational cost. Even our Small variant (ExDA -S) exceeds full MoCE-IR in both PSNR (+0.18 dB) and FLOPs (27G vs. 75G). These results validate that our design achieves strong restoration quality without sacrificing computational efficiency.
C ADDITIONAL EXPLANATION OF THE NONLINEAR VALUE PATH
This section provides more detailed explanation of the operator-leved effect and motivation behind introducing a nonlinear value transform into Restormer- style channel-wine attention.
Structural limitation of linear-value attention. As discussed in Sec. 3.1 of the main paper, when the value projection strictly linear, the attention operator fundamentally constrained to lie within
the linear span of the input features X. For any attention head, we may write the attention operator as:
Attn(Q Q K, V)= AV,
A Softmax (Q
(6)
Since V is linear transformation of X, its output is restricted by:
Range( (ttnn) C span(XX,
limitation that cannot be removed by widening the FFN adding post-aggregation nonlinearities, as these operations act after the linear mixing in Eq. 6.
Effect of the residual nonlinear value transform. To relax this structural constraint, the proposed module applies a residual non-affine transform to the value branch:
V + go
(8)
where de includes desth-wise convolutions and GELU activation. Because go is non-affine, have:
go g span(X),
(9)
which enlarges the admissible value subspace:
span(X)) Ç span (X' U go())).
(10)
This an operator-level property that directly increases the functional rank of the attention mechanism, independent of the global universal- approxximatio capacity of the entire network.
Empirical validation. Sec. 3.2 of the main paper provides evidence of the limitations of linear-value attention through synthetic function fitting and MNIST restoration experiments.
D ADDITIONAL ANALYSIS
To complement the quantitative stride ablation in the main paper, we further visualize Fig. B how the GST-driven attention maps change with different stride values {1, 8) across the five degradations (noise, blur, haze, rain, low-light). clear trend emerges: (i) 8 preserves the most spatial detail but also introduces redundant high-frequency responses; (ii) = maintains fine structures while producing clean and well-localized degradation cues; (iii) larger strides (s=44 4 and 8= overly downsample the spatial statistics and yield noticeably blurred weakened attention responses. These observations align with our quantitative findings, showing that s s= 2 provides the best balance between preserving degradation-relever information and avoiding redundancy.
E ADDITIONAL. VISUAL. RESULTS
E.1 DEGGRAAATIO
We additionally put more visual results on 3 Degradation settings, please refer to Fig, C for more detailed information.
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(3096)
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Figure B: Qualitative comparison of GST-driven attention maps across different stride values For five degradations (noise, blur, haze, rain, and low-light), we visualize the GST heatmaps obtained with E (1, 8,8) Smaller strides preserve finer spatial cues, while larger strides yield overly smoothed responses. Please zoom in for more details.
E.2 5-DEGRADATION
We additionally put more visual results on 5 Degradation settings, please refer to Fig. D for more detailed information.
E.3 COMPOSITED DEGRADATION
We additionally put more visual results on Composited Degradation settings, please refer to Fig. E more detailed information. Here, we selected the scenes composited by lowlight, haze and snow or lowlight, haze and rain, which are the most difficult settings. Our results indicated that our method can easily handle difficult and severe weather degradation.
E.4 ADVERSE WEATHER REMOVAL
We additionally put more visual results real-world WeatherBench data, please refer to Fig. F for more detailed information. Our results indicated that our method can handle different difficult and severe weather degradations.

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Oograded Input
ArrNet
Prompai
MoCE-IR 1R
ExDA (Ours)
DuNolla
Duitain
Figure C: More qualitative comparisons under 3-deg setting. Zoom in see more details.
E.5 REAL- WORLD WEATHERBENCH
additionally put more visual results on real-world WeatherBench data, please refer to Fig, G for more detailed information. Our results indicated that our method can easily handle difficult and severe real-world weather degradation.
E.6 MEDICAL IMAGE RESTORATION
We provide additional visual results for medical image restoration. For better visualization, we display 64x64 crops. Both our method and the state-of-the-an (SOTA) baseline, AMIR, effectively enhance image quality. However, our approach achieves better detail, producing clearer tissue structures and sharper boundaries.
F
USE OF LARGE LANGUAGE MODELS (LLMs).
Use of Large Language Models (LLMs). We used large language model (ChatGPT) solely to aid in polishing the writing and improving the readability of the manuscript, such refining grammar, style, and clarity. The model was not involved in idea generation, experimental design, implementation. analysis, or any other research-related aspects this work. All scientific contributions, including problem formulation, methodology, experiments, and conclusions, are entirely the responsibility of the authors.

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Degraded Input
MoCE-IR
(Ours)
Deblur
Deblur
Deblur
Lowlight
Lowlight
Lowlight
Figure D: More qualitative comparisons under 5-deg setting. Zoom in to see more details.

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Degraded Input
MoCE-IR
ExDA (Ours)
GT
LHR
LHR
LHR
LHS
LHS
LHS
Figure E: More qualitative comparisons under composited degradation setting. Zoom in to more details.
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Degraded Input
Histoformer
ExDA (Ours)
Rain
Rain
Rrin
RainDrop
Snow
Snow
Figure More qualitative comparisons under adverse weather. Zoom in to see more details.

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Degraded Input
WSWSNet
Hetoformer
Ad3R
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Daiaas
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Figure G: More qualitative comparisons under weatherbench data. Zoom in see more details.
(a)
(b)
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Figure H: More qualitative comparisons under medical restoration MRI super-resolution setting.
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(a)
(b)
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Figure I: More qualitative comparisons under medical restoration CT-Denoising setting.
LQ Input
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Figure More qualitative comparisons under medical restoration PET-Synthesis setting.
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Reviews:
[{"review_id": "6RaJxj80d0", "reviewer": "Reviewer_F6pD", "summary": "This paper targets all-in-one blind image restoration (IR) and argues that the dominant Restormer-style channel attention is hurt by two under-studied limitations: (i) the value path is strictly linear, so the attention head can only select but never transform features, and (ii) no global slot is available to encode degradation context. The authors plug two light-weight, backbone-agnostic primitives into any Restormer-like block.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper explicitly diagnoses the “linear-value” bottleneck of channel-wise attention in IR and connects the absence of a CLS-like token to the difficulty of inferring unknown degradations. The proposed fixes are minimal and can be dropped into existing models without architectural surgery.", "weaknesses": "1. The theoretical justification is limited. While the authors quote universal-approximation arguments, no formal proof is given that the residual value transform enlarges the hypothesis space of the entire Restormer block, nor that GST tokens are minimal sufficient statistics for degradation type. \n2. The experiments are not sufficient. For example, all experiments are conducted on 128 × 128 or 256 × 256 crops. The paper does not report run-time or memory on >4 K images where Restormer is usually applied. Additionally, some latest all-in-one methods are not compared in the experiments. Please refer to the survey paper (A survey on all-in-one image restoration: Taxonomy, evaluation and future trends. TPAMI, 2025) for more competing methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "9ztgNOMknt", "reviewer": "Reviewer_QUUL", "summary": "This paper revisits attention mechanisms for all-in-one blind image restoration and identifies two overlooked bottlenecks in Restormer-style architectures: limited expressivity from linear value aggregation and the absence of explicit global context. The authors propose two minimal, backbone-agnostic modules, a nonlinear value transform and a global spatial token, to address these issues. The method achieves consistent improvements across diverse benchmarks with negligible overhead. Overall, the work is well-written, experimentally solid, and offers a clear, though modest, conceptual contribution to the design of efficient all-in-one restoration models.", "questions": "1. Could you add a short explanation or table (in text form) that summarizes how the nonlinear value transform changes feature distribution or rank, even qualitatively?\n\n2. Since the paper claims to be backbone-agnostic, it would help to briefly discuss how the same ideas might work on other architectures such as NAFNet or SwinIR (I believe at least 1 degradation type analysis should be doable).\n\n3. Please include a small table with FLOPs, inference time, or memory to support the efficiency claim\n\n4. For the global spatial tokens, if figures cannot be shown, a short textual description of how different tokens behave under different degradations would be informative.\n\n5. It would also help to add a short discussion on how the proposed modules might generalize to unseen or mixed degradations in real-world conditions.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper targets a relevant and timely problem in all-in-one image restoration and presents clear motivation.\n\n2. The proposed modifications are simple but meaningful, addressing expressivity and global context in a principled way.\n\n3. Experiments cover diverse benchmarks and consistently show improvements, supported by ablation and diagnostic analysis.\n\n4. The method is lightweight and practical, showing good trade-offs between performance and complexity.\n\n5. The paper is clearly written and easy to follow.", "weaknesses": "1. The explanation about how the nonlinear value path improves expressivity is mostly intuitive, without quantitative or theoretical evidence.\n\n2. All experiments are based on a Restormer-type backbone, so it is unclear if the same gains would appear on other architectures.\n\n3. The claim of `negligible overhead` is not backed up by runtime or FLOPs comparisons.\n\n4. The paper does not analyze what the proposed global tokens actually learn, which would help readers understand their role.\n\n5. Most datasets are synthetic or composited; there is little discussion about generalization to real-world, uncontrolled degradations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "EHbmm7FADA", "reviewer": "Reviewer_8DjP", "summary": "The paper revisits Restormer-style channel-wise attention for All-in-One IR and pinpoints two bottlenecks: a purely linear value path that restricts outputs to the input span, and the absence of an explicit global slot to encode degradation context. It proposes two minimal, backbone-agnostic primitives: a nonlinear value transform applied before aggregation, and a Global Spatial Token that injects an explicit, content-adaptive global slot into attention. Through extensive experiments, ExDA reports consistent gains with negligible overhead.", "questions": "1. How do post-aggregation nonlinearity or beefed-up FFN/MLP compare, keeping compute similar? This would confirm the specific benefit of pre-aggregation nonlinear-V. \n2. Please add t-SNE/UMAP with NMI/ARI and visualize GST-driven attention for noise/blur/haze/rain and mixtures; analyze stride s beyond the current setting.\n3. Provide resolution/model-size trade-off curves for ExDA-T/S/Base and recommended configs.\n4. If a frequency-domain loss is used, how sensitive are results to its weight and which component (nonlinear-V or GST) benefits most? \n5. Have you tested on non-Restormer variants (e.g., U-former-style) or combined ExDA with frequency/prompt modules to show compatibility?\n6. More recent works can be added for comparison such as Perceive-IR (TIP’25) and DFPIR (CVPR’25).", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper precisely connects linear-V and missing global slots to AiOIR failure modes, then remedies them with pre-aggregation nonlinear-V and GST—small, principled changes rather than wholesale redesigns. Both primitives are lightweight and easily inserted into Restormer-like stacks without destabilizing training.\n2. Results span 3D/5D, compound (e.g., CDD11), adverse weather, underwater, and medical data, showing consistent improvements. \n3. Multiple FLOPs/parameters of ExDA size (e.g., the base, small and tiny models) help us evaluate deployment scenarios.", "weaknesses": "1. Additional controls comparing post-aggregation nonlinearity and stronger FFN/MLP capacity would better isolate the unique effect of pre-aggregation nonlinear-V.\n2. t-SNE/UMAP plots with NMI/ARI, and GST attention maps for different degradations/strengths, would make “degradation-aware” behavior more concrete.\n3. Provide resolution–quality/latency and model-size–quality/latency curves to guide engineering trade-offs across ExDA-Tiny/Small/Base.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "CsKFu9mUOP", "reviewer": "Reviewer_iMdM", "summary": "This paper proposes ExDA, a lightweight and backbone-agnostic framework for all-in-one blind image restoration (IR). The authors identify two bottlenecks in Restormer-style architectures: (1) linear value paths that limit expressivity and (2) the absence of global degradation-aware context. To address these, they introduce two modules — a nonlinear value transform (NVT) and a global spatial token (GST) — which enhance expressivity and degradation-awareness without significant computational overhead. Extensive experiments on synthetic, mixed, real-world, and medical benchmarks demonstrate consistent improvements over recent methods such as PromptIR, AdaIR, and MoCE-IR.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The design is simple, modular, and generalizable — the proposed primitives can be integrated into various backbones.\n* The paper clearly articulates two structural limitations in popular IR architectures (linear value paths and missing global slots) and proposes minimal, interpretable solutions.", "weaknesses": "* The contribution, while practical, feels **architecturally incremental** rather than conceptually transformative. The nonlinear value transform is essentially a lightweight convolutional enhancement inserted into the attention value path, similar in spirit to prior nonlinear attention variants. Likewise, the global spatial token extends the CLS-token concept rather than introducing a fundamentally new paradigm for degradation modeling. The theoretical discussion on “expressivity expansion” and “degradation-awareness” is more intuitive than rigorously grounded.\n\n* Although the comparisons are broad, the evaluated baselines are largely regression-based approaches, focusing on architectures. Incorporating comparisons with distribution-oriented models, e.g., DA-RCOT [1] or Defusion [2] would strengthen the paper's generality. \n\n* The related work can be enhanced with discussions of recent expressivity-enhanced attention variants and degradation-aware architectures, which are directly relevant to the claimed contributions.\n\n[1] Tang et al., Degradation-aware residual-conditioned optimal transport for unified image restoration, TPAMI 2025.\n\n[2] Luo et al., Visual-Instructed Degradation Diffusion for All-in-One Image Restoration. CVPR 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)