Efficient Degradation-agnostic Image Restoration via Channel-Wise Functional Decomposition and Manifold Regularization
Authors: Bin Ren
Yawei Li
Xu Zheng
Yuqian Fu
Danda Pani Paudel
Hong Liu
Ming-Hsuan Yang
Luc Van Gool
Nicu Sebe
Venue: ICLR 2026 Poster (2026)
Keywords: Image Restoration
Transformer
Contrastive Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Degradation-agnostic image restoration aims to handle diverse corruptions with one unified model, but faces fundamental challenges in balancing efficiency and performance across different degradation types. Existing approaches either sacrifice efficiency for versatility or fail to capture the distinct representational requirements of various degradations. We present MIRAGE, an efficient framework that addresses these challenges through two key innovations. First, we propose a channel-wise functional decomposition that systematically repurposes channel redundancy in attention mechanisms by assigning CNN, attention, and MLP branches to handle local textures, global context, and channel statistics, respectively. This principled decomposition enables degradation-agnostic learning while achieving superior efficiency-performance trade-offs. Second, we introduce manifold regularization that performs cross-layer contrastive alignment in Symmetric Positive Definite (SPD) space, which empirically improves feature consistency and generalization across degradation types. Extensive experiments demonstrate that MIRAGE achieves state-of-the-art performance with remarkable efficiency, outperforming existing methods in various all-in-one IR settings while offering a scalable and generalizable solution for challenging unseen IR scenarios.

Published as a conference paper at ICLR 2026
EFFICIENT DEGRADATION-AGNOSTIE IMAGE RESTORA- TION VIA CHANNEL-WIN FUNCTIONAL DECOMPOSI- TION AND MANIFOLD REGULARIZATION
Bin Ren
Yawei Li Xu Zheng*
Yuqian Fu Nicu Sehe2
Danda Pani Paudel
Hong Liu
Ming-Hsuan Yang" Luc Van Gool
"Mohamed bin Zayed University of Artificial Intelligence University of Trento 'HKUST (GZ) SINSAI, Sofia University "St. Kliment Ohridski"
EETHH Zürich
Peking University University of California, Merced
Notee Ionee
Grand Truhs
Rain lpper
Ground Truth
Low--Err--Stux Mixell
Underwater Image
Castrol
Castrol
MoCE-IR
MIRAGE
McCE-RR
MIRAGE
MIRAGE
MIRAGE
Castrol
Castrol
(4) Dznosing
(b) Denining
Composited IR
(6) Undarwater Edlanee
Aveuge (Ave) Perforaance Compotian for Degradation Digeadation Conposite Devradation, Acnoral, Underwter Imsaee Enhenceeet.
Figure 1: (a)-(d): Visual comparison for Denoising, Deraining, Composited Degradations (low-light, haze, and snow), and underwater image enhancement. (e): The average PSNR and SSIM comparison across 4 challenging all-in-one and zero-shot settings (Please zoom in for a better view).
ABSTRACT
Degradation-agnostic image restoration aims handle diverse corruptions with one unified model, but faces fundamental challenges in balancing efficiency and per- formance across different degradation types. Existing approaches either sacrifice efficiency for versatility fail to capture the distinct representational require- ments of various degradations. We present MIRAGE, an efficient framework that addresses these challenges through two key innovations. First, propose a channel-wise functional decomposition that systematically repurposes channel re- dundancy in attention mechanisms by assigning CNN, attention, and MLP branches to handle local textures, global context, and channel statistics, respectively. This principled decomposition enables degradation- superior secciency-performmane
learning while achieving trade-offs. Second, we introduce manifold regu- larization that performs cross-layer contrastive alignment in Symmetric Positive Definite (SPD) space, which empirically improves feature consistency and general- ization across degradation types. Extensive experiments demonstrate that MIRAGE achieves state-of -oe--o--thhtr---- performance with remarkable efficiency, outperforming existing methods in various all-in-one IR settings while offering a scalable and generalizable solution for challenging unseen IR scenarios.
indicates corresponding author: Hong Liu dhongliu@pku.ed...

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Published as a conference paper at ICLR 2026
INTRODUCTION
Image Restoration (IR) aims to recover clean images from inputs degraded by diverse worldworld corrptiion such as noise, blur, haze, rain, and low-light conditions (Zamir et al., 2022; Li et al., 2023a; Ren et al., 2024; Potlapalli et al., 2024). A central challenge is degradation-agnoon developing single model that can generalize across heterogeneous degradations. Despite recent progress, existing approaches often face an efficiency-peffrmmanne performance dilemma. On the one hand, heavyweight designs based on prompts, instructions, large vision-language models provide versatility but incur substantial computational cost (Potlapalli et al., 2024; Zamfir et al., 2025; Jiang al., 2025). On the other hand, lightweight solutions improve efficiency at the expense of restoration quality (Li ct al., 2022: Tang et al., 2025b). Achieving both robustness and efficiency within a unified framework remains an open problem.
restoration:
This difficulty can be better understood from two complementary perspectives. First, different degra- dation types impose fundamentally different representational requirements: additive corupttion (e.g.. noise, rain) primarily affect local textures, multiplicative distortions (e.g., haze, low-light) require global context modeling, and kernel-based degradations (e.g., blur) call for multi-scale structural reasoning. At the same time, basic architectural modules exhibit distinct inductive biases: convolu- tional filters excel at local texture modeling, attention mechanisms capture long-range dependencies, and MLPs enhance channel statistics. This motivates the insight that effective restoration model should systematically align distinct modules with complementary representational functions. Second, recent studies reveal substantial redundancy in attention- dimension (Venkataramanan et al., 2024; Dong et al., 2021). Many channels encode overlapping information, suggesting that this redundancy could be repurposed rather than discarded. Leveraging this observation allows for architectures that remain compact while preserving expressive capacity. These observations highlight that unified IR benefits not only from adding new modules, but from principled reorganization of existing capacity based on redundancy patterns and complementary in- ductive biases. This perspective motivates our design philosophy in MIRAGE, where representational roles are explicitly aligned with structural evidence rather than heuristic module stacking.
models, particularly along the channel
Building on these insights, we present MIRAGE, an efficient framework for gegradattionagnossee image restoration. MIRAGE introduces two components. (i) Channel-wise functiooal decomposition, where the input feature map partitioned along the channel dimension and processed by three specialized branches: convolution for local textures, attention for global context, and MLP for channel statistics. This structured decomposition repurposes redundant capacity into complementary roles, yielding both interpretability and strong perriency-peffommance trade-offs. (ii) Manifold regularization, a cross-layer contrastive strategy that leverages natural feature pairs within the model. Inspired by deeply supervised networks (Lee al., 2015), hypothesize that natural contrastive pairs exist between shallow and latent representations. Shallow features preserve fine spatial details but sensitive noise, while latent features are more abstract and semantically stable; aligning them encourages more robust shared representations. Importantly, rather than computing contrastive loss in Euclidean space, which may distort similarity when comparing structured representations. we operate in the Symmetric Positive Definite (SPD) manifold space. This formulation provides a more faithful alignment of representations, leading to improved generalization across degradation types. Overall, MIRAGE provides structurally grounded view of unified IR, where representational capacity is allocated and aligned based on statistical evidence at both the spatial and depth levels.
Extensive experiments across five degradation settings show that MIRAGE achieves sfate-of-the-art performance with remarkable efficiency: our model has only 6M parameters, more than five times smaller than recent prompt-basee based baselines, while also generalizing well to unseen scenarios such as underwater image enhancement. Both the visual and per-setting PSNR results are shown in Fig. 1.
Our contributions are summarized as follows:
We propose principled channel- functional decomposition strategy that aligns convolu- tion, attention, and MLP with distinct representational roles, enabling efficient and effective degradatiin-agnostic restoration.
We introduce manifold regularization through cross-layer contrastive alignment between shallow and latent features. We exploit natural contrastive pairs within the model, and per-
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form this alignment in the SPD manifold space rather than Euclidean space, providing more faithful representation similarity and improved generalization across diverse degradations.
We conduct comprehensive experiments across single, mixed, and unseen degradation settings, establishing MIRAGE as a strong and practical baseline for all-in-one IR.
RELATED WORK
Image Restoration with Various Architectures. IR addresses the ill-posed problem of retoring high-quality images from degraded inputs and has long been a core task in computer vision with broad applications (Richardson, 1972; Banham Katsaggelos, 1997; Xie al., 2025: Li al., 2023b; Zamfir et al., 2024). Early methods relied on model-based formulations with handcrafted priors, but deep learning has shifted the field toward data-driven approaches, including regression- based (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 al., 2023: Wang al., 2023b: Luo et al., 2023; Yue et al.. 2023; Zhao et al., 2024). These methods employ diverse backbones: convolutional networks for local structures (Dong al., 2015; Zhang et al., 2017b;a; Wang al., 2018), MLPs and state space models for channel or sequential dependencies (Tu et al., 2022; Guo et al., 2024a; Zhu et al., 2024; Gu & Dao, 2023; Dao Gu, 2024; Tang et al., 2025a), and Transformers for long-range interactions (Liang et al., 2021; Ren et al., 2023; Li et al., 2023a; Zamir al., 2022; Dosovitskiy et al., 2020; Liu et al., 2023; Shi et al., 2025), achieving promising results. Despite these advances, most IR solutions remain degradation-speciff
addressing tasks such as denoising (Zhang al., 2019), dehazing (Wu et al., 2021), deraining (Jiang et al., 2020), or deblurring (Kong et al., 2023), motivating the need for unified frameworks that generalize across diverse degradations while remaining efficient.
Degradation-agnosti Image Restoration. While training task-specific models for individual degradations can be effective, impractical to maintain separate models for each corruption. Real-world images often suffer from mixed degradations, making independent treatment infeasible, and task sesk-specifii approaches further increase computational and storage costs, amplifying their environmental footprint. To overcome these limitations, the emerging field of setraadationaggnoste IR focuses on single-blind models capable of handling multiple degradation types simultaneously (Zamfir et al., 2025; Zeng et al., 2025; Zheng et al., 2024b; Ren et al., 2026). For example, AirNet (Li et al., 2022) achieves blind All-in-One image restoration by using contrastive learning to derive degradation representations from corrupted images, which are then leveraged to reconstruct clean images. Building on this, IDR (Zhang et al., 2023) tackles the problem by decomposing degradations into fundamental physical components and applying two-stage meta-learning strategy. More recently, the extra learnable prompt-bast based paradigm (Potlapalli et al., 2024; Wang et al., 2023a; Li et al., 2023c; Tian al., 2025) has introduced visual prompt learning module, enabling single model better handle diverse degradation types by leveraging the discriminative capacity of learned visual prompts. Extending this idea, some works further model prompts from a frequency perspective (Cui et al., 2025) or propose more complex architectures with additional datasets (Dudhane et al., 2024). However, visual prompt modules often result in increased training time and decreased efficiency (Cui et al., 2025). Meanwhile, inspired by recent advances in self self-supervised learning, several works (Wu et al., 2021: Chen al., 2022c) have explored contrastive objectives to enhance low-level representations. though mainly within single-task IR scenarios. For the degradation-agnosti setting (Jiang et al., 2025; Li et al., 2022; Chen et al., 2025b; Zhang et al., 2025), the most recent DA-RCOT (Tang et al., 2025b) introduces contrastive loss applied to residual feature space, illustrating that contrastive signals can also benefit unified IR models. In contrast, our work aims to improve the model's s ability to capture representative degradation cues within the SPD space without relying heavy or complex prompt designs. Our goal in this work is to develop a Ietradatiionagnosse image restorer that remains both computationally efficient and environmentally sustainable.
ARCHITECTURES FOR IR 3 PRELIMINARY: DEGRADATION- AWWWHARAARCCHEETTTEEEEEE
Image Degradation and Restoration, Image restoration seeks to recover a clean image x from a degraded observation y:
D(x) n,
(I)
where D()) denotes a degradation operator and n noise. Real-world degradations are additive (e.g., Gaussian noise, rain: y x+)n), multiplicative le.g., haze, speckle: y x-m), convolutional
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Tau
Pack
00A3
%
Sax.
1x. Scale
Encoddr KDAS
Deoola MBWA
W c.
(a) (1-1) Mapping
QAET
2nd Scale
Ebcoder MIlAA
Llccnder MILAAI
(1/3 Wir
(b) a-d Mayping with Prompt
Corrr
Erooda MDDB
Doada MDAB
(R/4
1-1 Mapping with Promut
(1 Mixed Degr resraddaienn. Adaption Block
4f Soale
Latesi MDDii
(M/6 W/26 aC)
(4] Contrastive Mapping (Mhrg))
(e) Overall
propased AnylR
Figure 2: (a)-fc): The most adopted all-in-one image restoration decoder pipelines. (d): The toy illustration of our SPD contrastive pipeline. (e): The overall framework of the proposed MIRAGE: i.e., a convolutional patch embedding, a U-shape encoder-decoder main body, an extra refined block, and the proposed SPD contrastive learning algorithm. (f): Structure of each mixed degradation adaptation block (MDAB).
(e.g., blur, super-resolution: y k + n) (He et al., 2025). These factors often co-occur and are spatially variant (Zhai et al., 2023), forming compound pipelines:
y = (x))(( + n.
Such complexity demands models that preserve local details while reasoning about global structures.
Architectural Biases for Degradation Modeling. Deep networks embody distinct inductive biases: CNNs capture local spatial patterns: yp " E X,, effective for uniform or spatially invariant degradations. Transformers exploit global self-attention: yi = Σ, aijj - V,, well-suited for non-uniform, structured degradations (e.g. haze, patterned noise). ML.Ps, especially token-mixing forms, apply flexible position-wise mappings: y W2 Φf [W, x), though with weak spatial priors.
Each paradigm shows strengths yet clear limitations CNNs excel in local fidelity, Transformers in global reasoning, and MLPs in flexible feature interactions, but lack inductive structure. Alone, they are insufficient for complex degradations and often parameter-heavy. Their complementarity motivates unified, degradation-awan -wwrre architectures that leverage all three for robust IR in the wild.
4 THE PROPOSED MIRAGE
The design of MIRAGE is guided by two empirical observations. (i) Attention features consistently exhibit low-rank channel redundancy across scales (Fig. 3), indicating that a non- trivial portion of the representational capacity can be reassigned without loss of expressiveness. (ii) Different degradations favor complementary inductive biases, i.e., local texture sensitivity, global contextual aggregation, and channel-statistical modulation. These observations motivate a principled partition of feature channels into convolutional, attention, and MLP pathways, allowing each subspace to specialize in the bias it best suited for while maintaining overall model compactness. In parallel, the depth-asymmetrric covariance structures of shallow and latent representations provide a natural basis for cross-layer alignment, for which the SPD formulation offers eecometry-preservieee representation.
Prior works either train separate model per degradation (Fig. 2a), adopt decoder designs that inflate parameters (Fig. 2b), or rely on large-scale prompt-based models with visual/textual cues (Fig. 2c). In contrast, we propose simple yet effective miixed-backbor architec- ture (Fig. 2d), which already forms strong restoration baseline (Sec. 4. and further enhanced by cross-layer contrastive learning in SPD space between shallow and latent features (Sec. 4.2).
4.1 MIXED DEGRADATION ADAPTATION BLOCK FOR DEGRADATION-AANOSTU IR
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Algorithm 1 Mixed Parallel Degradation Adaptation
Require: P'm Li
Input features from three branches D Final fused output
Ensure: Fout
[Att] Attention Path
1: Q, K, + Linear( I pal
B Projection to attention tokens Multi-head self-attention
2: Fam Softmax OK )
vd
[Conv] Dynamic Convolution Path
3: F" +- ConvIx1(Ne) (m)m))
Normalization and expansion Gating, intermediate, convolutional paths b Content- depthwise conv Gated local enhancement Residual projection
4: 7. 1, a
5: a' DynamicDephhwiseConn/)
6: F s- (T/-T)- Concat [3, a')
7: FOORY f- Conv (onv1x1(F)) Fronv
[MLP] MLP Path
8: Four MLP(
Channel-wise transformation brings more non-linearity
[usion] Inter-Branch Mutual Fusion
9: Fat' < Fatt + Xaul o FFoay Feul))
b. Fuse conv and MLP into attention
out
10: Fronn t Foorr + >coom ((Fait + Fuuley
b Fuse attention and MLP into cony
out
F pamip' pomulp + >mmmp o ( F
b. Fuse attention and conv into MLP
Output Projection
pluse &- Project(Concatl (1 preonv I 1-mmlp'
Final unified representation
13: return 1 pluue
out
Redundancy in MHAs Opens Oppor- tunities for Hybrid Architectures. Re- dundancy has long been recognized as fundamental limitation in multi-head self-attention (MHA), the core building block of Transformers, in both NLP and vision domains (Nguyen et al., 2022b;a; Xiao al., 2024; Brödermann 2025; Wang et al., 2022; Venkatara- manan et al., 2024). Prior studies in- dicated that not all attention heads con- tribute equally, i.e., some are specialized and crucial, while others can be pruned with negligible impact. This inherently implies redundancy in the chunnel dimen- sion, as MHA outputs are concatenated along this axis. To empirically verify this redundancy in the context of IR, analyze intermedi- ate features from a lightweight attention-only model (details in the Appendix A). Specifically, we compute cumulative explained variance via PCA and normalized singular value spectra via SVD across multiple feature scales. Fig. 3(a) shows earlier scales (e.g.. 1st Scale) need far fewer principal components retain most variance, suggesting high redundancy. Fig. 3(b) further supports this, with a sharper singular value decay at shallower stages, indicating stronger low-rank structure in channel-wise representations. Even at the deepest stage (e.g., 4th Scale), achieving 90% variance requires only 31 of 192 components (= 16%), confirming redundancy persists throughout.
al.,
(6)
Figure 3: Channel redundancy analysis across multiple feature scales. (a) Cumulative explained variance curves from PCA applied the channel dimension of features from 1-4 scales and one latent scale. (b) Normalized singular value spectra (in log scale) of the same features via SVD. Latent feature in both plots means the channel- wise projected 4th Scale feature.
This insight motivates a departure from traditional head/channel pruning. Instead of discarding redundant capacity, we propose to repurpose it by splitting the channel dimension into three parts and feeding them into distinet architectural branches, i.e., attention, convolution, and MIP. This hybrid formulation leverages complementary inductive biases and makes full use of available representational space, offering a principled and efficient alternative to the previous pure MSA-based designs.
Parallel Design Brings More Efficiency. As shown in Lines 1-8 of Alg. 1, we instantiate this idea through a structurally parallel design that simultancously exploits complementary inductive biases. As illustrated in Fig. 2(f), the input feature Fin Rxxwxx is evenly partitioned along the channel dimension into three sub-tensors (i.e., 1 prson and pumpp) .), which then processed in parallel by
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(c)
Figure 4: (a)- The channel-wise similarity matrix from the 1st Scale (H x W C) to the 4th Scale (#/8 x w/s x 8C). (e): The channel-wise similarity matrix of (d) after channel-wise projection.
attention, convolution, and MIP branches. Each branch operates only on its allocated fraction of channels, substantially reducing computational cost, while its architectural heterogeneity enriches the representational space. This parallel decomposition achieves favorable balance between efficiency and expressiveness, in contrast to prior designs that rely on purely attention- based processing.
Inter-Branch Mutual Fusion Injects Expressivity Before FFN. While the parallel design improves efficiency and modularity. it reduces interaction across branches. To mitigate this, Lines 9-13 of Alg. 1 introduce an inter-branch fusion mechanism, where each branch enhanced via gated aggregation of the rest, modulated by learnable coefficients X. This introduces cross-path context blending, reinforcing feature complementarity before unification, forming an effective pre-FFN encoder.
Compared to the attention-only models, the fused output in Alg. I introduces richer interactions. This enhances the model's s ability to fit complex degradation mappings, making it more suitable for mixed ambiguous degradations. Subsequently, layer normalization, feed-forward network (FFN), and a residual connection are applied: Four = FFN (Norm/ Fflre) + Fluse. This sequence stabilizes feature distributions and further boosts expressiveness.
4.2 SHALLOOW-LTENN CONTRASTIVE LEARNING VIA SPD MANIFOLD ALIGNMENT
The unified IR model requires single backbone to process degradations that depend on fundamentally different representational levels. Shallow layers primarily encode degradation-specific fine-grained structures, whereas deeper layers become more semantic and statistically stable. This inherent depth asymmetry introduces representation drift when multiple degradations share the same feature space, motivating a mechanism that explicitly enforces cross-stage consistency. We therefore treat shallow and latent features as complementary views of the underlying signal and align them to stabilize the shared representation space, thereby improving generalization across heterogeneous degradations.
Shallow-Latent Feature Pairs are Naturally Contrastive Pairs, Features extracted at different depths exhibit fundamentally different statistical properties. As shown in Fig. 4, shallow-stage features (e.g., Scalel) present sparse and decorrelated channel distributions, while deeper layers (e.g., Scale4) become increasingly redundant and concentrated. This trend is quantitatively supported by the effective rank ratio across scales, which increases from only 4.2% (2/21 at 1st Scale) to 16.1% % (31/192 at 4th Scale). However, by compressing the deep features through a lightweight MLP, obtain latent representation with notably higher rank ratio of 36.5% (35/96), indicating more decorrelated and expressive embedding. This structural disparity between sparse, localized shallow features and compressed, semantic latent ones naturally defines a contrastive pairing without requiring augmentation. We leverage this depth-asym asmmeeri contrast to impose consistency across stages, enabling better semantic alignment and stronger representational generalization under complex degradation conditions. Note that this study is conducted under noise degradation; however, similar trends are consistently observed for other degradations as well. See the appendix for more details.
SPD Manifold Space Contrastive Learning Leads to More Discriminative Representations. To enhance representation consistency across depth, we introduce contrastive objective defined over SPD (Symmetric Positive Definite) manifold features. We note that the goal here is not to perform full Riemannian optimization along SPD geodesics. Instead, we adopt a lightweight formulation that retains the key second-order structure of covariance matrices while keeping training stable and efficient. Strict geodesic contrastive learning typically requires repeated log/exp mappings and matrix decompositions, which incur considerable overhead in large low-level vision models. Our approach strikes a practical balance by preserving essential SPD structure before projection. Specifically, in our work, given shallow features Fshallow E RC, (CC,HxXV HwW and latent features Fiatent E G xw", we first reduce their channel dimensions via convolutions. The resulting tensors reshaped into

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feature matrices X,, X1 RCXN with N = H x W, and their second- order statistics are computed as:
C. 1 N (X,
1
-X=--(X- Ma)(, us) + eel C N' 1 (X--)(xx(
(x)) + eI, (3)
where l is the mean across spatial dimensions, and eI ensures numerical stability and positive definiteness. The SPD matrices Cs, C₁ E RCXC are vectorized and projected to a contrastive embedding space via shallow I-layer MLPs:
NWrm vec(C.)), 2 N--NoowWW (W, vec(C:)),
(4)
where Wg. Wi are learnable projection layers, and Norm(-) denotes fg-nommalization. We then apply an InfoNCE-style contrastive loss to align the shallow and latent embeddings:
exp (sim(Zz, 21)/T)
SPD log Σ Exx (sim(2,, 2,, 2()/T)'
(5)
where sim(..) .) denotes cosine similarity and T temperature parameter. Unlike Fuclidean contrastive learning, which views features as flat vectors, our SPD-based method preserves second-order channel dependencies, providing richer structural supervision. This regularization aligns local and semantic features across depth, enhances discriminability, and introduces additional inference cost.
EXPERIMENTS
We conduct experiments adhering to the protocols of prior general image restoration works (Potlapalli et al., 2024; Zhang et al., 2023) under settings: (a) 3 Degradations), (b) 5 Degradations), (c) Mixed Degradation, (d) Adverse Weather Removal, and (e) Zero-Shot. The inplementation and experimental details, and the dataset description are provided in the appendix. Our code, checkpoints, and visual results are available via: https: //i thub. com/ Amaz z hengree/MRRRa
5.1 SOTA COMPARISON.
3 Degradations. We evaluate our method against others listed in Tab. 1, all trained on three degrada- tions: dehazing, deraining, and denoising. MIRAGE consistently outperforms all the comparison methods, even for those with the assistance of language, multi-task, or prompts. Notably, even our 6M tiny model outperforms our baseline PromptIR by 0.71dB on average. Our 10M small model achieves the best performance across all the metrics, with 60% fewer parameters compared MoCE-IR. Compared to DA-RCOT (Tang et al., 2025b), which performs contrastive learning over residual feature space, MIRAGE achieves consistently better restoration quality while using substantially fewer parameters (10M vs. 50M). This highlights the efficiency and effectiveness of our SPD-based cross-layer alignment despite its more compact design.
5 Degradations. Extending the 3 tasks with deblurring and low-light enhancement (Li et al., 2022; Zhang et al., 2023), evaluate our MIRAGE 's performance in more challenging 5-degradation setting. Tab. 2 shows that MIRAGE -S surpasses PromptlR (Potlapalli et al., 2024), MOCE-IR- (Zamfir et al., 2025), AdaIR (Cui et al., 2025), and VLU-Net (Zeng al., 2025) by 1.53dB, 0.6dB, 0.48dB, and 0.57dB on average, with fewer parameters. Our tiny model (6M) also achieves a second-best average PSNR against MoCE-IR (25M) and surpasses all other methods, including those aided by additional modalities, multi-task learning, or pretraining.
Mixed Degradations. To better approximate real-worle conditions, we extend OneRestore (Guo et al., 2024b) to cover both diverse single degradations (rain, haze, snow, low light) and composite cases with multiple degradations per image, yielding eleven distinct restoration settings. shown in Tab. 3, MIRAGE consistently outperforms leading approaches including AirNet (Li et al., 2022), PrompuR (Potlapalli et al., 2024), WGWSNet (Zhu et al., 2023), WeatherDiff (Özdenizci & Legen- stein, 2023), OneRestore (Guo et al., 2024b), and MoCE-IR (Zamfir et al., 2025), Specifically, our Tiny (6M) and Small (10M) models outperform OneRestore (Guo et al., 2024b) (6M) by 0.39 dB and 0.86dB average. Compared to the recent SOTA MoCE-IR (Zamfir et al., 2025) (11M), our Small model achieves 0.28dB higher performance with fewer parameters (10M vs. 11M). These results highlight the effectiveness of our method, partieularly for complex mixed degradations.
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Table 1: Comparison to state-of-the-art on three degradations. PSNR (dB, f) and SSIM (t) metries
are reported on the full RGB images. Best performances is highlighted. '-' means unreported results.
Dehazing
Deraining
Denoising
Method
Venue.
Params.
Average
SOTS
Rain100L.
BSD66
BSD68
BSD68,
BRDNet (Tian et al., 2020)
NN'20
23.23
.95
27.42
895
32.26
898
29.76
836
26.34
.693
27.80
.843
LPNet (Gao et al., 2019)
CVPR'D0 19
20.84
828
24.88
784
26.47
.778
24.77
.788
21.26
.52
23.64
.738
FDGAN (Dong ct al., 2020)
AAAI'20
.299
933
30.25
28.81
868
26.43
.766
28.02
883
DL (Fan et al. 2019)
TPAMI 19
2M
26.92
931
32.62
931
33.05
914
30.41
861
26.90
740
29.98
.876
MPRNet (Zamir al., 2021)
CVPR'21
16M
25.28
.95
33.57
954
33.54
.27
30.89
880
27.56
.779
30.17
889
AirNet
al., 2022)
CVPR'22
9M
27.94
.92
34.90
.967
33.92
.933
31.26
888
28.00
.797
31.20
.910
NDR (Yao et al., 2024)
TIP"24
28M
25.01
8600
28.62
848
28.72
826
27.88
.798
26.18
.720
25.01
.810
PromptlR (Potlapalli
2024)
NeurIP5'23 23
36M
30.58
.944
36.37
.922
33.98
31.31
.799
32.06
.913
McCE-IR IR S (Zamlir et all,, 2025)
CVPR'25
11M
30.9%
.779
38.22
983
34,08
933
31.42
888
28.16
.798
32.57
.916
AdaIR (Cui et al., 2025)
29M
31,06
980
38.64
983
34.12
935
31,45
.892
28.
.002
32.69
.918
McCE-IR (Zamfir et al., 2025)
CVPR'25
25M
31.34
.799
38.57
984
34.11
932
31,45
888
28. 18
800
32.73
.917
DA RCOT (Tang et al., 2025b)
TPAM
50M
31.26
.977
38.36
983
33.98
9344
31.33
890
28. 10
801
32.60
.917
MIRAGE -T' (Ours)
ICLR'26 ICLR'26
6M
31.81
.982
38.44
983
34.05
935
31.40
.892
28. 14
802
32.77
.919
MIRAGE (Ours)
10M
31.86
.981
38.94
.985
34.12
.955
31.46
.891
28.19
.803
32,91
.919
Methods with the assistance of vision language, multi-task learning, natural langunge prompts, or multi-modal control
DA-CLIP (Lao et. al, 2024)
ICLR'24
125M
29.46
963
36.28
968
30.02
821
24.86
5585
22.29
.476
Prm ne zt (Wi et al., 2024)
ACM MM'24
36M
30.83
.79
37.94
982
34.06
934
31.42
891
28. 14
801
32.49
.917
InstruetR-3DD (Conde et al., 2024)
BCCV'24
16M
30.22
959
37.98
978
34.15
933
31,52
890
28.30
804
32.43
.933
UniProccasor (Duan et al., 2025)
BCCV"24
1002M
31.66
979
38.17
982
34.08
935
31.42
891
28.17
803
32.70
.918
NeU (Zeng et al., 2025)
CVPR'25
35M
30.71
980
38.93
984
34.13
935
31.48
892
804
32.70
.919
RamiR (Ran et al., 2025a)
2pplicd'25
21.7M
31.29
977
38.16
.81
34.04
931
31.61
891
28.19
801
32.65
.916
Table 2: Comparison to state-of-the-art on five degradations. PSNR (dB, 1) and SSIM (1) metrics are reported on the full RGB images with (*) denoting general image restorers, others are specialized all-in-one approaches. Best performance is highlighted.
Dekazing
Deraining
Denoising
Deblurring
Low-Light
Method
Venue
Params.
Average
SOTS
Rain
BSD66,.-2
GoPro
1.0lvl
NAFNet" (Chen et al., 2022n)
22CV
17M
25.23
939
35 56
967 31.02
8833
26.53
.00
20.49
809
27, 76
8811
DGUNer" (Mou al., 2022)
CVPR'22
24.78
.900
31.10
27.25
21.87
28.32
.891
SwinIR" (Liang et al., 2021)
ICCVW'21
IM
21.50
891
30.78
923
30.59
868
24.52
773
17.81
773
25.04
.835
Restormer" (Zamir et al., 2022)
CVPR/22
26M
24.09
.927
34.81
962
31.49
884
27.22
829
20.41
880
27, 97.60
.881
MambalR' (Gno al., 2024n)
ECCV'24
27M
25.81
.94
36.55
971
31.41
884
28:61
.85
22.49
832
28.97
.901
DL (Pan et 2019)
19AMI'19
2M
20.54
.826
21.96
.722
23.09
.745
19.86
.672
19.83
.712
21.05
.743
Wealswellher
CVPR'22
38M
21.32
.885
29.45
9005
29.00
.841
25.12
.757
21.21
.722
25,22
.836
TAPE (Liu et al., 2022)
ECCV'22
1M
22.16
.861
29.67
904
30.18
855
24.47
.763
18.97
.621
25.09
.801
AirNet al. 2022)
CVPR'22
9M
21.04
.884
30.91
24.35
25.49
IDR (Zhang al., 2023)
CVPR'23
15M
25.24
.94
35.63
965
31.60
887
27.87
846
21.34
826
28.34
.893
PromptR (Potaapalii al. 2024)
NeurIPS'23
36M
30.41
.972
36.17
970
31.20
.885
27.93
881
22.89
829
29.72
.911
MoCE-IR-S (Zamlir et al., 2025)
CVPR'25
11M
31.33
.988
37.21
.988
31.25
.884
28.90
.877
21 21.68
851
30,08
.913
AdaIR (Cui ot al., 2025)
ICLR'25
29
30.53
.78
38.02
981
31.35
889
28. 12
858
23:00
845
30.20
.910
MoCE-IR (Zamlir et al., 2025)
CVPR'25
25M
30.48
.974
31.34
23.00
30.58
.919
DA RCOT (Tang el al., 2025b)
TPAM1'25
50M
30.96
975
37.87
980
31,23
858
28.68
.82
23.25
836
30.40
.911
MIRAGE. -T (Ours)
ICTR 22
6M
31.35
.79
38.24
.93
31.35
.91
27.
.850
23.11
.854
30.41
.912
MIRAGE S (Ours)
ICLR'26
10M
31.45
.980
38.92
.985
31.41
.892
28.10
.888
23.59
.888
30.68
.914
Methods with the assistance of natural language prompts multi task learning
InstructRR-55 5D (Conde al., 2024)
BCCV'24
16M
36.84
.973 27.10
31.40
23,00
29.55
.908
Artp-ampe7n n. (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
8846
VLU-Net (Zlng el al., 2025)
CVPR'25
35M
30.84
.980
38.54
982
31.43
891
27.46
840
22.29
833
30.11
.905
RamIR (Tang et 2025a)
Applicd
21.7M
31.09
.988
37.56
31.44
886
28
.88
22.02
30.18
.910
Adverse Weather Removal. Following (Valanarasu et al., 2022; Zhu et al., 2023), We test our MIRAGE on three challenging deweathering tasks: snow removal, rain streak and fog removal, and raindrop removal. Tab. shows the comparison of our MIRAGE and other sfate-of-thh-ar methods. MIRAGE consistently outperforms existing methods across almost all datasets except PSNR for RainDrop. The performance gains over multiple weather degradations demonstrate the effectiveness of MIRAGE in handling diverse weather conditions. Especially, 0.30dB improvement on PSNR over Histoformer (Sun et al., 2024) and 1.05dB improvements over MPerceiver (Ai et al., 2024).
Zero-Shot Setting. We evaluate method's generalization under challenging zero-shot setting with real-world underwater images. As shown in Tab. 5, MIRAGE - achieves 17.29 dB and 0.773 SSIM, surpassing MoCE-IR (Zamfir et al., 2025) by +1.38dB PSNR, while being more compact.
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Table 3: Comparison to state-of-the-arl on composited degradations. PSNR (dB, 1) and SSIM (t) reported on the full RGB images. Our method consistently outperforms even larger models, with favorable results in composited degradation scenarios.
Cingl-Single
CDDIT-Dauble
CDDII-Triple
Method
Params,
Arg.
Low (L)
Haaee
Rain (R) Snow
L+81
L+R
L+S
H+R
11+S
Lell+R
L+i++S
AirNet
9M
24.83
778
24.21
951 26.55
891
26,
919
23.23
779
22.82
710
23.29
723
22.21
868
23.29
901
21
708
22.24
725
23.75
.14
Promgui
26.32
26.10
969
31.56
946
31.53
960
24.49
789
24.51
761
24.54
924
23,70
.12
23,23
747
850
WGWSNet
26M
24.39
774 27.90
982
33,15
964
34.43
973
24.27
800
25.06
772
24.00
765
27.23
955
27.65
950
23,90
772
23.97
771 26.96
863
WeatheDDil
83M
23.58
763
21.
904
24.85
585
24.80
588
21.83
756
22 69
730
22 12
707
21 25
868
21.99
868
21
716
21.04
898
22.49
790
Onekesture
6M
26.48
826
990
33.40
964
34,31
822
799
739
957
30.21
964
788
791
878
27.26
824
970
817
26.25
800
966
970
770
881
MIRAGE (ours)
27.13
830
32.39
989
969
35.57
978
26
823
26.21
507
26
7799
29
960
29.72
9967
25.17
.793
25:41
7793
28
883
MIRAGE (ours)
10M
27.41
J833 33.12 .992 34.66 .971
35.98
9881
26.55
5288 26.53
510 26.33
5903. 30.32 .965 30.27 .969 25.59 .801 25.86 7799 29,33 8877
Table 4: Comparisons for 4-task adverse weather removal. Missing values are denoted by" '-'-
Method
Yenue
Snow100K-S
Snow100K-L
Outdoor-Rain
RainDrop
Average
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
All-in- (Li et al., 2020)
CVPR'20
28.33
882
24.71
.898
31.12
.927
28.05
.902
TrathWeather (Valanarasu et al., 2022)
CVPR'22
32.51
.934
29.31
888
28.83
.900
30.17
.916
30.20
.909
et (Chen al., 201226)
CVPR'22
34.42
.915
31.81
.925
WGW (Zhu et al., 2023)
CVPR'23
34.31
946
30.16
.901
29.32
.921
32.38
.938
31.54
.966
WeatherDiffes (Özdenizei & Legenstein, 2023)
TPAM1'23
35.83
.957
30.09
.044
29.64
.931
30.71
.931
31,57
.911
WeatherDiffias (Ondenizei Legenstein, 2023)
TPAM1'23
35.02
952
29.58
.894
29.72
-922
29.66
.93
31,00
.923
AWRCP (Yo el. 2023)
ICCV'23
36.92
.65
31,92
.944
31.39
.933
31.93
.911
33.04
.911
GridFormer (Wang al., 2024)
IICV'24
37.46
31.71
.93
31.87
.933
32.39
996
13,36
.939
MPercciver (Ai ct al., 2024)
CVPR
36.23
.957
31.02
.16
31.25
.925
33,21
.299
32,93
.932
DTPM (Ye et al., 2024)
CVPR
37.01
9666
30.92
.917
30.99
9944
32.72
944
32,91
.440
Histofonner (Sun al. 2024)
ECCV'24
37.41
966
32.16
.966
32.08
.939
33.06
.44
33.68
.944
MIRAGE (Ours)
ICLR'26
37.97
.93
32.33
.999
32.82
.949
32.78
.945
33.98
.949
Table 5: Zero-Shot DonsaiD Un-
Table 6: Complexity Analysis. FLOPs are computed on an image of size 224 x 224 using a NVIDIA Tesla A100 (40G) GPU.
derwater Image Enhancement Results.
Method
PSNR (f)SSIM (T)
SwinlR (Liang al., 2021)
15.31
.740
Method
PSNR Meyooy Paruns. (L)FLOPs (L)
NAFNet (Chu et al., 2022)
15.42
.744
AinNet at 2022)
31.20
238G
Restormer (Zamir al., 2022)
15.46
.755
PrempilR (Polspalli et al., 202())
32,06
9830M
35.59M
132G
AirNet (Li 2022)
15.46
.45
IDR (Zhing et al. 2023)
4905M
15.34M
98G
AdalR (Cui at., 2025)
9740IM
28.79M
124G
IDR (Zhang et al., 2023)
15.58
.662
McCE-IR-S (Zamlir et al., 2025)
32,51
4263M
1146M
37G
PrompeR (Potlapalli et al. 2024)
15.48
.488
McCE-IR (Zamfir et al., 2025)
32.73
6654M
25.35M
75G
MoCF-IR IR (Zamfir et 2025)
15.91
.765
MIRAGE -T (Ours)
32,77
3729M
6.21M
16G
MIRAGE -S (Ours)
17.29
.773
MIRAGE (Owrs)
32.91
4810M
9.68M
Importantly, our model never sees underwater data during training, yet our adaptive modeling not only fits mixed degradations but also transfers robustly to unseen conditions, Besides, we also followed the same experimental setting introduced by UniRestore Chen et al. (2025a) for the generalization ability evaluation. Meanwhile, the real-world evaluation presented in Tab. D shows that MIRAGE generalizes reliably to real-world, camera-captured degradations.
Efficiency Comparison. Tab. 6 compares PSNR, memory, parameters, and FLOPs. Our Tiny model (MIRAGE-T) -T), with only 6.21M parameters and 16G FLOPs, delivers the best efficiency-perfrrmaae trade-off, outperforming all prior methods, including larger models like PromptIR (Potlapalli et al., 2024) and MOCE-IR-S (Zamfir et al., 2025). It surpasses MOCE-IR-S by +0.26 dB while using less than half the computation, and even our Small variant (MIRAGE -S) exceeds full MOCE-IR in both PSNR (+0.18dB) and FLOPs (27G vs. 75G). These results confirm that our design achieves strong restoration quality without compromising efficiency.
Visual Comparison. MIRAGE effectively restores fine structural details and reliably suppresses subtle visual artifacts across diverse and unseen degradations (Fig. and appendix).
5.2 ABLATION ANALYSIS & DISCUSSION
Components ablation. Tab. 7 shows starting from an attention-only setting (32.23 dB, 19.89M), we progressively integrate each module while reducing complexity.
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Removing the dynamic convolution branch (wo DynamicCon) causes a 0.56 dB drop, indicating its importance for local spatial modeling. The channel-wise MI.P (w/o C-MLP) also plays critical role, with 0.38 dB performance loss. Naive concate- nation (w/o Fusion) leads a further 0.20 dB drop, confirming that explicit feature integration more effective. On the regu- larization side, removing contrastive learn- ing (w/o CL. SPD) or replacing SPD with Euclidean alignment degrades performance
Table Ablation Study of MIRAGE -T under the Degradation Setting with Tiny model.
Results
Ablaton
Params.
PSNR (dB. T)
SSLM(L)
att-only (Ourx)
19.89 M
32.23 (.0.54)
.912
w/o DynamicCony
9.43 M
32.21 (40.56)
.111
who C-MLP
7.01
32.39 (-0.38)
we Fusion (ie. CatO-Oaly)
5.71 M
32,57 (40.20)
.944
weo CL SPD weo SPD (CL Euclidean)
5.80M
32.63 (-0.14)
.966
6.10M
32.53 (-0.24)
.914
MIRAGE TT (Full)
6.21M
32.77
.919
by 0.14 dB and 0.24 dB, indicating that structure-agnostee contrastive learning can misguide opti- mization, while manifold-aware alignment provides consistent benefits. Overall, each component contributes to the final performance. Our full model offers the best balance between accuracy and efficiency with only 6.21M parameters and 32.77 dB PSNR.
Why shallow- Contrastive Aligmment Matters.
Nel Contrall
Different degradations rely different feature levels: de- noising and deraining benefit from shallow, texture-rich features, while dehazing and low-light enhancement re- quire deeper semantic features; deblurring needs both. This heterogeneity makes unified modeling challenging. We therefore introduce contrastive aligmment between shal- low and latent stages to encourage semantic coordination. When shallow features dominate (e.g., denoising), align- ment guides latent features to be more task relevat;;;;n;; when latent features dominate (e.g., dehazing), shallow features inherit semantic consistency (Bertasius et al., 2015). Fig. 5 validates that contrastive alignment improves shallow-latent correlation, validating its necessity for cross-degradation generalization.
Cooine Smilmmy (1)
9.7
0.5
Dn-015 0h:
3rig5o
Denhn
Deface
Figure 5: Shallow-latent cosine simi- larity across degradations. Contrastive alignment improves feature correlation.
Why Euclidean Fails and Why SPD Works? (De- raining Case Study) Euclidean contrastive learning col- lapses shallow-latent alignment by enforcing indiscrim- inate similarity, reducing both diagonal and off-diagonal terms to trivial constants, and erasing task cues. SPD, by aligning covariance matrices on a Riemannian manifold, preserves second-order dependencies and guides updates along meaningful directions. In the deraining case (Fig- ure 6), Euclidean CL. degenerates into near-constant simi- larity (off-diag 0.00237, ratio 0.99), while SPD maintains diagonal dominance and non-trivial off-diagonal structure (0.0787, ratio 0. 0.149), producing coherent patterns.
UKidgg Stop
Figure 6: Shallow-laten similarity under three settings: (a) w/o CL (unstructured; off-diag 0.0784, ratio 0. 0.148), (b) Eu- clidean CL (collapsed; off-diag zu 0.0024, ratio 0.99), (c) SPD CL (preserved; off- diag 0.0787, ratio 0.149).
6 CONCLUSION
We presented MIRAGE, an efficient framework for degradation-agnostic image restoration that achieves favorable balance between robustness and efficiency. Through channel-wise functional decomposition, the model repurposes redundant capacity into convolution-, attention-, and MLP. based branches, enabling complementary modeling of local textures, global context, and channel-wise statistics. To further enhance gross-degradation generalization, we introduced manifold regularization, aligning shallow and latent features in the SPD manifold space for more consistent and discriminative representations. Extensive experiments across diverse degradations, including mixed and unseen scenarios, demonstrate that MIRAGE achieves tfate-of-the-as performance. Inspired by the metaphor of mirage, i.e., revealing the hidden reality beneath visual distortions, framework learns ionndaatio-annostte representations by balancing global, local, and channel-wise information, providing scalable foundation for future research in degradation-agnostee IR.
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ACKNOWLEDGMENTS
This work was partially supported by the FIS project GUIDANCE (Debugging Computer Vision Models via Controlled Cross-modal Generation) (No. FIS2023-03251),
ETHICS STATEMENT
Our work focuses on general-purpose image restoration, aiming to improve efficiency and robustness across diverse degradation types. The intended positive impact includes deployment in low- rowrresource or safety-critices scenarios such as mobile photography, remote sensing, medical imaging, and environmental monitoring. At the same time, recognize that improved restoration techniques could be misused for deceptive content editing or large-scale surveillance. We encourage responsible of our method and provide models and code with appropriate licenses and documentation to support transparency and ethical adoption. No personally identifiable or sensitive data were used in this research.
REPRODUCIBILITY STATEMENT
We aim to ensure reproducibility and transparency of our results. The MIRAGE framework is imple- mented in PyTorch with standard training protocols and evaluation metrics. Detailed descriptions of the architecture, training settings, datasets, and baselines supplementary material. Upon acceptance, we will release the full code, pretrained models, and instructions for reproducing all reported results, including ablation studies and comparisons. Random seeds and hardware details are also documented to facilitate faithful replication.
provided in the main paper and
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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 et al. (2010) and WED Ma et al. (2016) datasets, and corrupt the images with Gaussian noise at levels E {15, 25, 50). BSD400 contains 400 training images, while WED includes 4.744 images. We evaluate the denoising performance on BSD68 Martin et al. (2001) and Urban 100 Huang et al. (2015). For single-task deraining. we use Rain 100L 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), consisting 72,133 135 training images and 500 testing images. Under the All-in-Ons setting, aforementioned training datasets for 120 epochs and directly test across all three restoration tasks.
train unified model on the combined set of the
5 Degradation Datasets. The 5-degradation setting is 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, we use the LOL-vI dataset Wei et al. (2018), consisting of 485 training images and 15 testing images. Note that for the denoising task under the 5-degradation setting, report results using Gaussian noise with a = 25. The training takes 130 epochs.
Composited Degradation Datasets. Regarding the composite degradation setting, we use the CDD11 dataset Guo et al. (2024b). CDD11 consists of 1,183 training images for: (i) 4 kinds of single- degradation types: haze (H), low-light (L), rain (R), and snow (S); (ii) 5 kinds of donble-degradatioon types: low-light + haze (l+h). low-light-rain (L+R), low-light snow (L+S), haze rain (H+R), and haze + snow (H+S). (iii) 2 kinds of drigle-degradattion type: low-light + haze + rain (L+H+R), and low-light + haze + snow (L+H+S). We train our method for 170 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. (2022) and WGWSNet Zhu et al. (2023), evaluating the performance of our approach on multiple synthetic datasets. We assess the capability of MIRAGE 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 Snow 100K Liu et al. (2018), Raindrop Qian et al. (2018), and Outdoor-Rai Li et al. (2019b) datasets. For testing, we evaluate our model the following subsets: Snow 100K-S (16,611 images), Snow 1 10K--L (16,801

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Table A: The details our the tiny and small version of our MIRAGE FLOPS are computed on an image of size 224 x 224 using a NVIDIA Tesla A100 (40G) GPU.
MIRAGE -T
MIRAGE-S
The Number of the MDAB crosses scales
[3,5,5.1
[,
The fnput Fmhedding Dimension
24
30
The FFN Expansion Factor
2
2
Number of the Relinement Blocks
Params, (3) FLOPs
6.21M
9.68
G7
images), Outdoor- (750 images), and Raindrop (249 images). Same Histoformer Sun et al. (2024), we train MIRAGE on "AllWeather" with 300,000 iterations.
Zero-Shot Underwater Image Enhancement Dataset. For the zero-shot underwater image enhance- ment setting, we follow the evaluation protocol DCPT JiaKui et al. (2025) by directly applying model, trained under the 5-degradation setting, on the UTEB dataset Li et al. (2019a) without any finetuning. UTEB consists of two subsets: 890 raw underwater images with corresponding high-quality reference images, and 60 challenging underwater images. We evaluate our zero-shot performance on the 890-image subset with available reference images.
A.2 IMPLEMENTATION DETAILS
Implementation Details. Our MIRAGE framework is designed be end-to-end trainable, removing the need for multi-stage optimization of individual components. The architecture adopts robust 4-level encoder-decoder structure, with a varying number of Mixed Degradation Attention Blocks (MDAB) at each level level-speeciee specifically [3, 5, 5, 7] from highest lowest resolution in the Tiny variant. Following prior works Potlapalli et al. (2024); Zamfir et al. (2025), we train the model for 120 epochs with batch size of 32 in both the 3-Degradation All-in-One and single-task settings. The optimization uses a combination of L, and Fourier loss, optimized with Adam Kingma & Ba (2015) (initial learning rate of 10 4, B1 0.9, 82 0.999) and cosine decay schedule. During training, we apply random cropping to 128 x 128 patches, along with horizontal and vertical flipping
as data augmentation. All experiments are conducted on a single NVIDIA H200 GPU (140 GB). Memory usage is approximately 42 GB for the Tiny (i.e., MIRAGE -T) model and 56 GB for the Small model (i.e, MIRAGE -S).
Model Scaling. We propose two scaled variants of our MIRAGE, namely Tiny (MIRAGE -T) and Small (MIRAGE -S). As detailed in Tab. A, these variants differ in terms of the number of MDAB blocks across scales, the input embedding dimension, the FFN expansion factor, and the number of refinement blocks.
A.3 OPTIMIZATION OBJECTIVES
The overall optimization objective of our approach is defined
(A) Here, Cyoureer 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.
Lital L1 + Xfre x Lrourier + >etrs LSPD-
Specifically, adopt E loss that adopted in IR tasks Potlapalli et al. (2024); Zamfir al. (2025); Li et al. (2022); Ren et al. (2026); Cui et al. (2025); Ren et al. (2024); Li et al. (2025), defined Li - 2/1, enforce pixel-wise similarity between the restored image & and the ground- truth image æ. Lrouriers utilized in MoCE-IR Zamfir et al. (2025); Cui et al. (2025), to enhance frequency-dome domain consistency, the real-valued Fourier loss, is defined as:
C Freat (f) (x)a( + [|Fimag - Fimag(Ez)l
(B)
where f and denote the restored and ground-truth images, respectively. F.eal:)) and Finag (.) represent the real and imaginary parts of the 2D real-input FFT (i.e., rfft2). The final loss is computed as the e distance between the real and imaginary components of the predicted and target frequency
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spectra. Same as MoCE-IR Zamfir et al. (2025), Arre is set to 0.1 throughout our experiments. Meanwhile, the LSPD is defined as in Eq. 3-5 of our main manuscript. More ablation studies regarding the proposed LsPD provided in Sec. C.3. The temperature parameter of the proposed LSPD is set to 0.1 throughout all the experiments.
B PRELIMINARIES ON SPD-BASED FEATURE STATISTICS
This section provides brief background on the concepts involved in our cross-layer alignment strategy. The intention is to supply intuitive context-rathen rrthher than additional derivations for second- order feature statistics, the SPD structure, and depth-asymmetric representations used in Sec. 4.2.
Second-order feature statistics. Raw activations capture local appearance, but the way channels vary together often reveals more stable information about degradations. For a feature matrix X E RCXN the covariance
N -=N-1(X-
(X- TT)
summarizes inter-channel relationships. Diagonal entries reflect each channel's variability, while off-diagonal entries describe redundancy and dependence patterns. These structures differ consis- tently across layers and degradations (Fig. Appendix Fig. B), making covariance compact and informative descriptor.
SPD property of covariance matrices. Covariance matrices are symmetric and positive definite by construction and therefore lie in the SPD set. This structure encodes meaningful geometric information: eigenvalues represent correlation strengths, and the matrix as a whole can be interpreted as a "shape" in channel space. Preserving this structure is important- direct Euclidean operations may flatten or distort correlation patterns, an effect also reflected in the collapse observed with Euclidean contrastive learning (Fig. 6).
Representing SPD matrices for comparison. To compare covariance matrices within a contrastive objective, we vectorize C and apply learnable projection. This retains second-order relationships while mapping them to an embedding space suitable for contrastive learning. Compared to raw feature vectors, covariance embeddings emphasize structural organization and therefore provide more stable alignment signal.
Depth-asymmettre representations. Shallow and latent features naturally exhibit different statistical behavior: shallow layers respond strongly to local degradations and show pronounced redundancy, while deeper layers become more decorrelated and semantically aggregated. Their covariance matrices reflect these differences in a consistent way across degradations, making shallow-latent pairs complementary views of the same signal and a natural target for alignment.
Intuition behind SPD-based alignment. Aligning covariance-based SPD embeddings focuses on how channels interact, rather than individual activation values. This yields supervision that is less sensitive to local noise and more reflective of the underlying representation structure. Encouraging shallow and latent features share similar second-order statisties stabilizes the shared feature space required for diverse degradations.
Overall, covariance provides a compact view of channel interactions, the SPD structure preserves meaningful second-orde -oddr relations, and depth- aepth-asymmetric covariance patterns naturally motivate the alignment strategy formalized in Sec. 4.2.
C MORE METHOD DETAILS & SUPPLEMENTARY EXPERIMENTS
C.I DEG. COMPARISON
Dingle-Degradation. Tab. B, we compare our method against state-of-the-arr approaches on single degradation tasks. For dehazing on SOTS dataset, we compare with DehazeNet Cai et al. (2016), MSCNN Ren et al. (2016), AODNet Li et al. (2017), EPDN Qu et al. (2019). FDGAN Dong et al. (2020), and all-in-one methods AinNet Li et al. (2022) and PromptIR Potlapalli al. (2024). Our 6M parameter model achieves competitive performance 131.46 dB PSNR. 0.977 SSIM), while our 10M model establishes new state-of-the-ar results (31.53 dB PSNR, 0.980 SSIM), outperforming the much
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Table Comparison state-of-the-art for single degradations. PSNR (dB, f) and SSIM (f) metries are reported on the full RGB images. Best performance is highlighted. Our method excels over prior works.
(a) Dehazing
(b) Deraining
(c) Denoising on BSD68
Method
SOTS
Method
Params
R0in
Method
a=15
c=:25
DehazeNet
22,46
.551
DIDMDN
23.79
373
DsCNS
33,89
933
31.23
M85
27.92
.789
MSCNN
22.05
UMR
32.39
IRCNN
33.87
.929
31.18
AODNet
EPON
22.57
MSPFN
EDGAN
23.15 9911
PKKe
23.15
021
BRDNet
34.10
9129
31.43
865
28.16
.744
34.14
935
AirNat
AinNa
9M
077
PronpeRR
038
31.71
PromptRR
X0M
31:31
.913
PrompelK
36M
37.04
579
PrompIR (Nepooduca)
36M
34.15
934
31.50
894
28.33
.00
MIRAGE (Ourz)
(owns)
6M
(ours)
34.13
996
26.36
MIRAGE (Owns)
MIR (Ohors
(Owss
10M
.937
31.65
Algorithm DynamicDepttwiseCCon
Require: a E XB
> Input feature map Output after dynamic depthwise conv
Ensure:
a' BXCXHX
[Step 1] Generate Dynamic Kernel
f- Adapt adaptivvevvgPool22in
D Global context pooling
2: K <- Conv2D(K, 1 x 1, out_ch = C)
Linear projection
3: + GELU(K)
Non-linear activation
4: K < Conv2D(K, x 1, out_ch = C. k2)
Generate kernel weights
5: K + Reshape(K, [B C, 1, k, R])
D Form depthwise filters
[Step 2] Apply Depthwise Convolution
6: Oda & Reshape( [1. B.C. C, H, W])
b Prepare for grouped conv Apply dynamic depthwise
7: d Conv2D K, groups B C, padding k + conv
8: a' Reshaapedhhu [B, C, 11 WI)
Reshape back to original shape
9: return a²
R.
$1
FPN
IFN
FDN
FPN
FFN
Degeditics Adipiis
Figure A: The illustration of different designs of the proposed MDAB.
larger PromptI (36M parameters). For deraining on Rain we evaluate against DIDMDN Zhang Patel (2018), UMR Yasarla Patel (2019), SIRR Wei al. (2019), MSPFN Jiang et al. (2020), LPNet Gao et al. (2019), AirNet Li et al. (2022). and PromptIR Potlapalli et al. (2024). Our method significantly outperforms all baselines, with our 10M model achieving 38.01 dB PSNR and 0.982 SSIM. For denoising on BSD68, compare with classical methods DnCNN Zhang et al. (2017a), IRCNN Zhang et al. (b)) FFDNet Zhang et al. (2018), BRDNet Tian et al. (2020), and recent all-in-one approaches AirNet Li et al. (2022) and PromptlR Potlapalli al. (2024). Our method consistently outperforms all competitors across different noise levels (a=15, 25, 50), demonstrating superior performance with significantly fewer parameters than existing all-in-one methods.
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Published as a conference paper at ICLR 2026
Algorithm B SPD Contrastive Learning Optimization Pseudocode
fen't encoder
fúct decoder
patch patch_ embedding: shallow convalutional patch embedding
refinement_ conu: the refinement block and the final convolutionn spd: corpute SPC feature
for in loader: load minibatch with samples
Fanalow - patch embeddingg (x) t Convolutional Patch Embedding Platese Jen (Fessii)))
C., C₁= spd (Forttoe), (pittt))) Compurte SPD (Symmetric Positive Dafinite) manifold feazures proj_norm (C,), proj_norm (C,) # Projection and normalize
Freees - fan (Fastan) refinement_ cone (Frecn))
L1 (x, Afre X Kroniee (x, $) X iRee 1Re,
tetal loss
I.backward() back-pzopagate
update Jeer patch_embeddding, refinement_come SGD update
def Croaiie: (a, bl:
Real-valued Tourier loss
Please refer to Bg.B our Appendix.
return loss
def Lus la, b): 52D Loss
Ploase refer to Rq.5 of our main manuscript.
return loss
C.2 DETAILS OF THE DESIGN FOR THE PROPOSED MIXED BACKBONE.
To investigate the effectiveness combining MI.,, convolution, and attention mechanisms, we conducted an extensive design-level ablation study. The quantitative results are presented in Tab. 7 of the main manuscript. Here, we provide detailed visual illustrations each design Fig. A.
C-MLP. To strengthen channel-wise representation, denoted C-MLP(). Given the input feature map fimlp E RBxCXHXW
introduce Channel-wise MLP module,
first flatten the x W. The C-MLP
spatial dimensions to obtain sequence pmipp E BxCxLL where implemented using two ID convolutional layers with a GELU activation in between. The GELU function introduces non-linearity, enabling the model to learn more complex and expressive channel- wise transformations. After processing, the output is reshaped back to the original spatial format, yielding sumpp E BxCxXXX
Dynamic Depthwise Convolution. The DynamicDepthhee wwn.hwisieCo(((( module is designed to capture content-adaptive local structures and is employed in Alg.1 of main manuscript. As detailed in Alg. A, the input feature a BBxCxxxW is first passed through global average pooling and two 1 convolutions to generate dynamic depthwise kernel for each channel and sample. The input is reshaped and convolved with the generated kernels using grouped convolution, enabling sample-specine spatial filtering. The resulting output a' maintains the original resolution while embedding adaptive local information.
C.3 DETAILS OF THE PROPOSED SPD CONTRASTIVE LEARNING.
As shown in Alg. B, our SPD-based contrastive learning aims to align shallow and latent representa- tions by operating the space symmetric positive definite (SPD) matrices. Specifically, given the shallow features extracted from the convolutional patch embedding and the latent features produced by the encoder, we compute their second-order channel-wise statistics to obtain SPD representa- tions. These matrices are then vectorized and projected through learnable MLP layers, followed by l normalization to form contrastive embeddings. An InfoNCE- loss is applied between the shallow and latent embeddings to encourage structural alignment across depth. This contrastive term complements the pixel-level and frequency- based objectives, promoting more discriminative and consistent feature learning without introducing any additional cost during inference. Importantly,
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1st-Scalo
2od-Scale
3rd-Scale
4th-Scale
Denoising
(b) Deraining
(c) Dehazing
Figure B: The cross-sclae channel-wise similarity matrix visualization for Denoising, Deraining, and Dehazing.
by leveraging the geometry of second-order feature statistics, our approach implicitly regularizes the representation space, encouraging intra-instance compactness and inter-degradation separability. This geometrically grounded formulation bridges low-level signal priors with high-level contrastive learning, offering principled and scalable solution to all-in-one image restoration.
C.4 ABLATION REGARDING THE OPTIMIZATION OBJECTIVES
Tab. C shows that replacing SPD-based
contrastive learning with a standard results in clear performance drop,
Euclidean-space contrastive loss (w/o SPD)
demonstrating the advantage of modeling second- order channel correlations on the SPD manifold rather than relying solely on first-order vector similarities. When the entire contrastive module is removed (w/o CL. & SPD), performance de- grades even further, indicating that aligning shal- low and deep features is essential for effective representation learning. Moreover, removing the Fourier loss (w/o Fourier Loss) slightly reduces performance, suggesting that frequency-domain supervision provides additional benefits. Overall, the full model achieves the best results, confirming the effectiveness of jointly optimizing spatial, frequency, and SPDD-manifold-based structural consistency. Note that throughout all the experiments, we set Actra 0.05 and 1.
Table C: Ablation Study of MIRAGE -T on Degradation Setting.
Results
PSNR (JB, t)
SSIM(L)
noo CL SPD w/e SPD
3.80M
(-033)
916
10M
(-0.24)
.14
wh Fourier Loss
5.80M
32.70 (-007)
.917
MIRAGE -T (Full)
6.2
32.77
.999
C.5 SHALLOW-LATENT FEATURE SIMILARITY
Besides the channel- wise similarity comparison provided in our main manuscript for denoising. We also find consistent findings in other degradation, i.e., raining and hazing. The corresponding channel- wise similarity across scales is provided in Fig. B. These observations reveal several important trends: (i) Despite the diversity of degradation types, consistent pattern emerges across scales. Specifically, from the first to the fourth scale, the overall channel-wise similarity indicates substantial redundancy among feature channels. After channel reduction, the latent features become more decorrelated, which validates the rationale for applying contrastive learning between the latent and shallow (i.e., first-scale) features. (ūi) Different degradation types exhibit varying degrees of channel redundancy.

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Published as a conference paper at ICLR 2026
Table Zero-shot evaluation on real-world under-display camera datasets TOLED and POLED (Zhou et al., 2021).
Method
TOLED (PSNR / SSIM /PPIS)
POLED (PSNR SSS///S///PPP
AirNet (Li al., 2022)
14.58 0.609 00.45
7.53/ 0.350/ 0.820
PromptIR (Potaaaali et al., 2024)
16.70/ 0.688 00,42
13.16 0.588 0.619
DiAUUR (heng et al, 2024a)
29.55 0.887 0.2811
15.62 0.424 / 00,505
MIRAGE-S (Ours)
28.01 0.881 0.2993
16.93 / 0.604 0.500
As illustrated Fig. B, hazy images tend to produce more inherently independent features, whereas rain-degraded inputs show strong channel-wise redundancy even in the latent space. This suggests that degradations like haze may benefit from larger embedding dimensions to capture more expressive representations, while simpler degradations (e.g., rain) can achieve effective restoration with smaller embedding sizes due to their inherently redundant structure.
These insights open up new directions for adaptive and degradation-aware model design in future research. Notably, this trend is not limited to the three representative samples shown; we observe similar patterns consistently across the dataset statistical sense. We plan conduct a more comprehensive and quantitative investigation of this phenomenon in future work.
C.6 MORE GENERLIZATION EVALUATION
To further assess generalization beyond synthetic settings, we evaluate MIRAGE-S on the real- world TOLED and POLED under-display camera datasets (Zhou et al., 2021). As shown in Tab. D, MIRAGE-S achieves strong performance across both benchmarks. On POLED, which contains more severe signal attenuation and non-linear spatial artifacts, MIRAGE-S clearly surpasses prior methods across all three metrics, indicating robust transfer to challenging real-world degradations. On TOLED, MIRAGE-S remains competitive and delivers results close to diffusion-bran based DIffUIR despite its significantly lower complexity. These findings suggest that the proposed mixed-backhond architecture and SPD>-based alignment maintain good stability under real sensor degradations and generalize reliably across distinct UDC hardware conditions.
D ADDITIONAL VISUAL RESULTS.
D.1 3 DEGRADATION
Fig. C presents qualitative comparisons representative cases of denoising, deraining, and dehazing, benchmarked against recent state-of-the-us methods. The proposed MIRAGE consistently yields more visually faithful restorations, characterized by enhanced structural integrity, finer texture details, and reduced artifacts. These results underscore the effectiveness of our unified framework in handling diverse degradation types while preserving high-freyuency information and geometric consistency.
D.2 DEGRADATION
For the 5-degradation setting, we provide visual comparisons for the low-light enhancement task in Fig. D. As illustrated, the proposed MIRAGE produces noticeably cleaner outputs with improved luminance restoration and better color consistency compared to MoCF-IRZamfir et al. (2025), demonstrating its robustness under challenging illumination conditions.
D.3 COMPOSITED DEGRADATION
Fig. E and Fig. F present visual comparisons under more challenging composite degradations, namely low-light + haze + snow and low-light + haze + rain, respectively. As observed, our method reconstructs significantly IR Zamfir et al. (2025) tends to produce noticeable artifacts and over-smoothed regions under these complex conditions.
scene details and preserves structural consistency, whereas MoCE-
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Notia Irper
PronpriR
McCE-IR
MIRAGE (Ohn))
Groon Truit
Ran lnper
ArNee
PrompiRR
McCE-IR
MIRAGE (Ohrg)
Grond Trunt
(b) Demining
Impat
Prompil
(Oan))
Grond Trush
00009
7734
Figure C: Visual comparison of MIRAGE with state-of-the the--tt--rrt art methods considering three degrada- tions. Zoom in for better view.
D.4
SERO-SHOT UNDERWATER IMAGE ENHANCEMENT
Fig. demonstrates that even when directly applied to unseen underwater images, our method is able effectively enhance visibility and contrast, producing results that noticeably clearer than the
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Low-light Input
MoCF-IR
MIRAGE (Ows)
Ground Truth
Sample: 111
Sample: 665
Sample:
Sample:
Figure D: Visual comparison MIRAGE with state-of-the. methods considering low-light degra- dation. Zoom in for better view.
Figure Visual comparison of MIRAGE with state-of-the-arr methods considering composited degradation (Low-light + Haze + Snow). Zoom in for a better view.
raw input and visually closer to the reference images. This qualitative evidence further validates the strong generalization ability of the proposed framework to unseen domains.

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Published as a conference paper at ICLR 2026
Sendgg
SIAGE
Figure F: Visual comparison of MIRAGE with state-of-the-an art methods considering composited degradation (Low-light + Haze + Rain). Zoom for a better view.
MIRAGE
HERAGE
Figure G: Visual results of MIRAGE for Underwater Image Enhancement. Zoom in for a better view.
E LIMITATIONS AND FUTURE WORK
While the proposed MIRAGE achieves new state-of-the-ar performance on most all-in-one image restoration benchmarks, we observe that its deblurring performance still lags slightly behind MoCE- IR Zamfir et al. (2025). We attribute this to the relatively compact model size of our current design, which favors efficiency over aggressive capacity. To address this, future work will explore scaling up the model size to be on par with larger architectures such as PromptIR Potlapalli et al. (2024), MoCE- IR Zamfir et al. (2025), and AdaIR Cui et al. (2025), aiming to further boost performance while maintaining the architectural elegance and efficiency of our design. Moreover, our current SPD-based contrastive learning leverages a conventional InfoNCE loss Euclidean space after projecting SPD features. While effective, it does not fully exploit the intrinsic geometry of the SPD manifold. As part future efforts, we plan to investigate geodesic-based contrastive formulations and Riemannian- aware optimization strategies, which may offer a more principled and theoretically grounded way to align structured representations across semantic scales. Additionally, different degradations may favor different proportions of convolution, attention, and MLP capacity. Learning such ratios dynamically an interesting direction and could further adapt MIRAGE to ceerradationsppciiie characteristics. We view this as a promising avenue for future research.
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F BROADER IMPACT
Image restoration (IR) is fundamental task with applications in photography, remote sensing, surveil- lance, autonomous driving, medical imaging, and scientific visualization. By proposing unified and efficient framework capable of handling diverse degradation types with minimal computational cost, our work may benefit scenarios where image quality compromised by environmental or hardware constraints, The lightweight design of MIRAGE further enables deployment on resource cnuurce-limited devices such as mobile phones, drones, or embedded cameras, which can support use cases in low- resource settings or critical domains like emergency response and environmental monitoring. From a research perspective, our modular design and SPDDbbsed contrastive formulation may encourage further exploration of geometrically-awwan representation learning in restoration and related areas.
G USE OF LARGE LANGUAGE MODELS (LLMS)
We used OpenAl's GPT-based Large Language Models (LLMs) (OpenAI, 2023; 2022) to polish the writing and improve the readability of the paper. The models were not used for developing the methodology, rumning experiments, analyzing results. All scientific contributions remain entirely the work of the authors.


Reviews:
[{"review_id": "RyfLIFB6Ft", "reviewer": "Reviewer_FzZr", "summary": "This paper proposes a unified image restoration framework, with channel-wise decomposition for different strategy, including convolution, attention, and MLP. Additionally, the contrastive loss is introduced between shallow and deep features to encourage shared representations. Experiments show effectiveness of the method.", "questions": "1. How to decide the channel role in channel-wise decomposition for convolution, attention, and MLP, is there any principle?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The writing is clear and easy reading.\n2.  Experiments show the effectiveness of the proposed method.", "weaknesses": "1. The motivation of this work that using different strategy to handle agnostic degradations is much more invested in previous research, and the innovation is somewhat minimal;\n2. There is no solid clarification that the shared representation between shallow and deep features are benefited for diverse degradations, which makes the contrastive objective not convincing, and the ablation improvement is minimal for probable performance jitter;\n3. The main experiments in Tab. 3 and 4 may not show the superiority of the proposed method with subtle improvements.", "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": "NbU6fMZCJB", "reviewer": "Reviewer_4QQN", "summary": "This paper proposes MIRAGE, an efficient degradation-agnostic image restoration framework based on channel-wise functional decomposition and manifold regularization in Symmetric Positive Definite (SPD) space. The method splits features into convolution, attention, and MLP branches to handle local textures, global context, and channel statistics, respectively. Additionally, a shallow-latent contrastive alignment in SPD manifold space is introduced to enhance generalization across heterogeneous degradation types. The authors evaluate MIRAGE across five restoration settings, including single degradation, mixed degradations, adverse weather restoration, and zero-shot underwater enhancement.", "questions": "See the above parts.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The SPD-space contrastive learning is a novel contribution that effectively preserves high-order statistics, outperforming Euclidean contrastive counterparts in both stability and generalization, as evidenced by ablation studies.\n2. The channel-wise functional decomposition is conceptually clear and well-motivated by empirical redundancy analysis. It provides an elegant way to repurpose redundant channels into complementary submodules, rather than simply increasing model capacity.\n3. The analysis of attention redundancy and shallow-latent feature disparity provides a strong justification for the proposed framework.", "weaknesses": "1. The two main contributions claimed in the introduction are largely based on the combination and engineering integration of existing techniques, rather than presenting any genuinely novel innovation or introducing a fundamentally new perspective.\n2. While the contributions are substantial, the paper is dense and may be challenging for readers unfamiliar with SPD manifolds or contrastive learning in feature covariance space. Some mathematical formulations could benefit from more intuitive explanations or visual aids.\n3. Although the paper claims efficiency advantages, it lacks direct comparison to recent diffusion-based restoration models that are increasingly influential in the community.\n4. The zero-shot evaluation is limited to a single domain. Including additional domains (such as low-light) would further validate the claimed generalization ability.\n5. The paper primarily focuses on average performance improvements, but does not discuss scenarios where MIRAGE underperforms or struggles, which would provide a more balanced understanding of robustness.", "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": "5fijwRTyet", "reviewer": "Reviewer_UuMU", "summary": "This paper proposes MIRAGE, a lightweight, efficient, and degradation-agnostic image restoration framework that unifies multiple degradations (noise, haze, rain, blur, low-light) under one architecture. MIRAGE achieves SOTA PSNR/SSIM across 3-, 5-, and mixed-degradation benchmarks, with significantly fewer parameters (6M–10M) and lower FLOPs (16G–27G). The model also generalizes to zero-shot underwater enhancement and adverse weather removal.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents a well-motivated and compact architecture that repurposes attention-channel redundancy through a channel-wise functional decomposition (Conv/Attn/MLP branches), aligning with the natural inductive biases of different degradations—local texture (Conv), global context (Attn), and channel statistics (MLP). \n2. The proposed SPD manifold alignment introduces cross-layer contrastive learning between shallow and latent features using covariance-based second-order statistics. This prevents feature collapse common in Euclidean contrastive learning and promotes consistent, degradation-agnostic representations. Ablations show clear gains.\n3. MIRAGE achieves consistent SOTA or near-SOTA performance across a wide range of benchmarks—3/5 degradation settings, CDD11 composite degradations, adverse weather, and zero-shot underwater enhancement—while being significantly smaller and faster than strong baselines such as MoCE-IR, PromptIR, and AdaIR, demonstrating an excellent efficiency–performance balance.\n4. The analysis of channel redundancy via PCA/SVD, shallow–latent similarity matrices, and qualitative contrastive alignment visualizations all support the design’s intuition.", "weaknesses": "1. While the paper’s SPD-based manifold regularization is empirically effective, its mathematical formulation remains heuristic. The method projects SPD matrices back to Euclidean space for InfoNCE optimization, without leveraging true Riemannian metrics or geodesic distances. As a result, the claimed “manifold alignment” lacks formal theoretical justification.\n2. The experiments comprehensively compare with transformer- and prompt-based all-in-one models (PromptIR, MoCE-IR, AdaIR), but omit recent diffusion-based or state-space approaches that are increasingly strong in low-level restoration.\n3. Although MIRAGE handles five degradation types and several composites, it is still trained on synthetic datasets. The paper would be stronger with real-world or camera-captured degradation benchmarks, or cross-domain generalization beyond underwater scenes.", "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": "8NblSHDG2V", "reviewer": "Reviewer_rX26", "summary": "This paper proposes MIRAGE, an efficient framework for degradation-agnostic image restoration. The key contributions include a channel-wise functional decomposition strategy that integrates convolution, attention, and MLP modules to handle diverse degradations, and a manifold regularization method using cross-layer contrastive learning in the Symmetric Positive Definite (SPD) manifold space. Extensive experiments demonstrate that MIRAGE achieves state-of-the-art performance with significantly fewer parameters and computational costs compared to existing methods, while generalizing well to unseen degradation scenarios.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The SPD manifold-based contrastive learning is a well-motivated approach for cross-layer feature alignment, effectively leveraging second-order statistics for more robust representation learning compared to Euclidean space.\n* The proposed channel-wise decomposition provides a principled and efficient method to harness the complementary inductive biases of convolution, attention, and MLPs, resulting in an excellent performance-efficiency trade-off.", "weaknesses": "* The experimental comparison in Tab. 1, while extensive, omits the cited approach DA-RCOT [1] that also employs a contrastive learning objective (over the residual feature space). A direct comparison would more firmly establish the advantages of the proposed SPD manifold approach over alternative contrastive formulations (SPD vs. residual feature space).\n\n* The related work section could be enhanced by discussing other contrastive paradigm in All-in-one image restoration, highlighting the contribution of the proposed cross-layer contrastive losses over the SPD feature space.\n\n* The motivation of introducing SPD features to enhance the generalization is not very explicit to the readers. Further explanations or adding a Teaser Figure at the beginning would strengthen the presentation.\n\n[1] Xiaole Tang, Xiang Gu, Xiaoyi He, Xin Hu, and Jian Sun. Degradation-aware residual-conditioned optimal transport for unified image restoration. TPAMI, 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)