Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation
Authors: Bin Ren
Eduard Zamfir
Zongwei Wu
Yawei Li
Yidi Li
Danda Pani Paudel
Radu Timofte
Ming-Hsuan Yang
Luc Van Gool
Nicu Sebe
Venue: Accepted by TMLR ()

Abstract: Restoring multiple degradations efficiently via just one model has become increasingly significant and impactful, especially with the proliferation of mobile devices. Traditional solutions typically involve training dedicated models per degradation, resulting in inefficiency and redundancy. More recent approaches either introduce additional modules to learn visual prompts, significantly increasing the size of the model, or incorporate cross-modal transfer from large language models trained on vast datasets, adding complexity to the system architecture. In contrast, our approach, termed AnyIR, takes a unified path that leverages inherent similarity across various degradations to enable both efficient and comprehensive restoration through a joint embedding mechanism, without scaling up the model or relying on large language models. Specifically, we examine the sub-latent space of each input, identifying key components and reweighting them first in a gated manner.  To unify intrinsic degradation awareness with contextualized attention, we propose a spatial–frequency parallel fusion strategy that strengthens spatially informed local–global interactions and enriches restoration fidelity from the frequency domain. Comprehensive evaluations across four all-in-one restoration benchmarks demonstrate that AnyIR attains state-of-the-art performance while reducing model parameters by 84% and FLOPs by 80% relative to the baseline. These results highlight the potential of AnyIR as an effective and lightweight solution for further all-in-one image restoration. Our code is available at: https://github.com/Amazingren/AnyIR.

Published in Transactions on Machine Learning Research (02/2026)
Any Image Restoration via Efficient Spatial-Frequency Degra- dation Adaptation
Bin 1.2
Eduard Zamfir'
Zongwei Wu Yawei Li Yidi Lis Nicu Sebe
Danda Pani Paudel
Radu Timofte
Ming-Hsin Hsuan Yang? Luc Van Gool
"University of Trento, IT Mohaaeed bin Zaged University of Artificial Intelligence, UAE
Unniversity of Würzbury, DE 'ETH Zürich, CH "Taiguan University of Technology, CN
INSAIT, Softa University "St. Kliment Ohridski", BC University of California, Mereed, USA
Reviewed on OpenReview: https: // openreview. net/ forun? id- RObgOpdDqr
Abstract
Restoring multiple degradations efficiently via just one model has become increasingly signif- icant and impactful, especially with the proliferation of mobile devices. Traditional solutions typically involve training dedicated models per degradation, resulting in inefficiency and redundancy. More recent approaches either introduce additional modules to learn visual prompts, significantly increasing the size of the model, or incorporate cross-modal transfer from large language models trained on vast datasets, adding complexity to the system architecture. In contrast, our approach, termed AnylR, takes a unified path that leverages inherent similarity across various degradations to enable both efficient and comprchensive restoration through joint embedding mechanism, without scaling up the model relying on large language models. Specifically, examine the sub-latent space of each input, identifying key components and reweighting them first in gated manner. To unify intrinsic degradation awareness with contextualized attention, we propose apatial-frequenc parallel fusion strategy that strengthens spatially informed local local-global interactions and enriches restora- tion fidelity from the frequency domain. Comprehensive evaluations across four all-in-one restoration benchmarks demonstrate that AnylR attains state-of-the redueing model parameters by 84% and FL.OPs by 80% relative to the baseline. These results highlight the potential of AnyIR effective and lightweight solution for further all-in-one image restoration. Our code is available at: https: //////ttttbbbb com/ /wt.a/aaaae//aaannaaaaaaaa AnyIR.
performance while
1 Introduction
Image restoration (f.e., IR) aims recover a clean image from its degraded observation, and a central challenge is how to handle multiple and diverse degradations within a unified framework. This problem is inherently ill-posed since multiple solutions may explain the same output, and thus effective priors learned representations are crucial for successful restoration. In real-world scenarios. degradations arise from heterogeneous sources (noise, blur, compression, weather artifacts, etc.), and practical systems must cope with them within a single pipeline rather than as isolated tasks. Meanwhile, multi-step or per-degradation pipelines introduce storage, routing, and computation overhead during both training and deployment, whereas single-checkpoint model better matches mobile and edge constraints and simplifies system integration. This motivates the pursuit of single efficient model capable of addressing multiple degradation types.
Despite significant advances, existing IR. methods still struggle to efficiently handle diverse degradations while preserving essential details Li et al. (2023a); Ren et al. (2024b;a); Wu et al. (2024c); Yin et al. (2024); Zheng et al. (2024b); Ding et al. (2024); Wu et al. (2024b); Li et al. (2025). Many current solutions roly on
This work was partially conducted during the visiting stay at INSAIT. 1 Corresponding author.

---

Published in Transactions on Machine Learning Research (02/2026)
per-degradation models or multi-stage pipelines, which introduce storage, routing, and computation overhead during both training and deployment, and are difficult to scale or deploy on mobile and edge platforms. In contrast, a single-checkpoint model better matches deployment constraints and simplifies system integration. This work addresses this gap by pursuing an efficient All-in-one IR model that can handle multiple degradation types within a unified poyresenaation-eaaaae
framework.
The complexity of restoration models Liang al. (2021); Zamir al. (2022; 2021); Wang et al. (2022); Chen et al. (2022) arises primarily from the diversity of degradations. Consequently, most methods are tailored to specific tasks with limited generalization. A versatile system often requires integrating multiple specialized models, leading to heavy frameworks. Some studies have shown that a single architecture can handle multiple degradations, but they lack parameter unification, producing multiple checkpoints for different tasks Chen et al. (2022); Ren et al. (2024a). This reduces efficiency despite simplifying the system. Recent research has sought architectural and parameter unification Li et al. (2022); Potlapalli et al. (2024); Zhang al. (2023); Liu et al. (2022). Diffusion-basse methods demonstrate strong generative capacity Ren et al. (2023b); Jiang et al. (2023); Zhao et. al. (2024), while prompt-based designs guide networks with modality-specific embeddings Wang et al. (2023a); Potlapalli et al. (2024); Li et al. (2023c). Other works leverage text as intermediate reprosentation Luo et al. (2024): Conde et al. (2024). Despite promising results, these approaches significantly increase model size, inference time, or rely on degradation-specifs supervision.
In this paper, we take a unified perspective: although each degradation has its characteristics, all restoration tasks share underlying principles of sepurating nuisance degradations from structural image information. From a learning viewpoint, this setting can be regarded as a puulti-envrronment learning problem, where degradations correspond to environments and the objective is to learn representation that is invariant to degradation-specifft nuisances yet sufficient for restoration. Unlike large-scale priors in LLMs, our method builds invariance directly from single-image cues, yielding both efficiency and strong generalization. To this end, we propose AnylR, a lightweight framework that integrates global context and local degradation-awan cues. Specifically, we introduce a gated local block that disentangles fine-grained degradation-awarr details (ego, shifled, and scaled parts), adaptively reweighted via gating, and a parallel allention pathway to caplure global dependencies. A spatial frequency fusion mechanism further intertwines the two representations, balancing structural integrity with fine detail recovery. Importantly, features are processed in sub-latent partitions before aggregation, reducing computational cost while retaining rich information. This design enables AnyTR to act as an implicit degradatiooninnaarie learner, effective and elficient across diverse restoration settings (see Fig. 4).
Our main contributions are summarized as follows:
We propose AnylR, a unified and efficient all-in-one IR. model that achieves superior performance while reducing computational cost. by 85.6% compared to state-of-the-art counterparts.
We design a novel local-global gated intertwining mechanism combined with cortiallfrequencyy fusion strategy, enabling cohesive and adaptive embeddings without degradation-speeiffe supervision.
Through extensive experiments on diverse restoration tasks, we demonstrate the effectiveness and efficiency of AnylR, providing strong and practical baseline for future research in all-in-one IR.
2 Related Work
Image Restoration (IR) Image restoration aims to solve a highly ill-posed problem by reconstructing high-quality images from their degraded counterparts. Due to its importance, IR has been applied to various applications Richardson (1972); Banham & Katsaggelos (1997); Li et al. (2023b); Zamfir et al. (2024); Miao et al. (2024); Zheng et al. (2024a). Initially, IR was addressed through model-based solutions involving the search for solutions to specific formulations. However, bearnnng-asse approaches have gained much attention with the significant advancements in deep neural networks. Numerous approaches have been developed, including regression-based Lim et al. (2017); Lai et al. (2017); Liang et al. (2021); Chen et al. (2021b); Li et al. (2023a); Zhang al. (2024) and generative model- based pipelines Gao et al. (2023); Wang et al. (2023b); Luo et al. (2023); Yue et al. (2023); Zhao et al. (2024); Liu et al. (2023) that are based convolutional Dong
2

---

Published in Transactions on Machine Learning Research (02/2020)
D2
E
D
E
E
D
Em
Dm
Em
(a) m (1-1) Mapping
(b) m-1 Mapping
(c) 1-1 Mapping with Prompt (d) 1-1 Mupping for All in One IR (Ours)
Figure 1: Mathematical formulations of mainstream paradigms for All-in-One image restoration. (a) m x (1-1)
mapping: each degradation type 2 requires an independent encoder decoder pair (E,, D,). (b) m-1 mapping
with shared decoder: multiple task-spocific encoders E, share a single decoder D. [c] 1-1 mapping with prompls: unified encoder decoder (F,, D) is conditioned mapping: pure unified framework where (E, D) directly learns degradationninvariia yet discriminative reprosentations, without prompts or multiple encoders.
external prompts p(=). (d) Our proposed 1-1
et al. (2015); Zhang al. (2017b;a); Wang et al. (2018), MLP Tu et al. (2022), state-space mode Guo al. (2024a); Zhu et al. (2024); Gu & Dao (2024); Dao & Gu (2024), or vision transformers-basee (ViTs) architectures Liang et al. (2021); Li et al. (2023a): Zamir et al. (2022); Ren et al. (2023a); Dosovitskiy et al. (2020). Although state-of-the-art methods have achieved promising performance, mainstream IR. solutions still focus on addressing single degradation tasks 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).
One for Any Image Restoration Training a task-specific model address a single degradation is effective, but impractical for real-world deployment due to the need for separate models for each corruption. In practice, images often suffer from multiple, overlapping degradations, making it inefficient to address them individually. Such task-specific solutions require significant computing and storage resources, and their environmental cost grows with the number of degradations. To overcome these limitations, the emerging All-in-One IR setting develops a single blind model that handles multiple degradations simultaneously, without. requiring task-specific specialization Tang et al. (2025); Jiang et al. (2025). Moreover, training a unified model across multiple degradations enables the learning of transferable structures, which facilitates adaptation new or mixed degradations through lightweight fine-tuning rather than training separate models from scratch. From learning perspective, this setting also be interpreted learning, where different degradations act environments and the challenge is to learn representations that are robust across them while still sufficient for reconstruction. Several representative approaches have been proposed. AirNet Li et al. (2022) employs contrastive learning to derive degradation-aware embeddings from corrupted inputs, which are then used to guide restoration. IDR Zhang et al. (2023) follows a meta-lcarning perspective, decomposing degradations into fundamental physical principles and adapting via a two-stage learning process. More recently, the prompt-based paradigm Potlapalli et al. (2024); Wang et al. (2023a); Li et al. (2023e) has been introduced, where learned visual prompts condition single model across degradations. Such prompls act as task embeddings that steer the network, with exlensions including frequency-aware prompts Cui et al. (2025) or more complex designs requiring additional datasets Dudhane et al. (2024). While effective, these approaches often increase training cost and reduce efficiency. To further enhance generalization, MoCE-IR Zamfir et al. (2025) explore a mixture-of ofoturee-f--xperts design, activating specialized subnetworks for different conditions. Although this improves performance, it also increases overall architectural complexity. In contrast, our work strengthens the model's ability to capture representative degradation information without the overhead of heavy prompts or expert routing, providing a simpler and efficient pathway for All-in-Oni IR.
form of multi-environment
3 AnylR
3.1 Preliminaries
Formally, for the image restoration problem, given an observation:
y (2r))) +
(1)
3

---

Published in Transactions on Machine Learning Research (02/2026)
where x denotes the latent clean image, D₂ is degradation operator parameterized by (e.g., noise, blur, haze, rain), and is an additive perturbation, the goal of IR is to recover x from its degraded counterpart y-
In practice, this inverse mapping is inherently ill-posed, as multiple clean images may correspond to the same degraded observation. In other words, different 21 and 22 may both satisfy + y. Therefore, IR. does not generally admit a mathematically unique solution. Instead of explicitly enforcing the invertibility of Dz, modern learning-base based approaches aim to estimate a statistically plausible reconstruction conditioned on y-
From a probabilistic perspective, the restoration model can be interpreted conditional distribution p(x: v). During training, supervision encourages convergence toward the ground-truth mode among feasible candidates, while at inference time, the prediction reflects a statistically consistent solution favored by the learned image prior rather than an arbitrary selection ion among multiple possibilities. This viewpoint aligns with the standard treatment of ill-posed inverse problems in the literature and explains why learning- beaning-basod IR models can produce stable and perceptually coherent reconstructions even under mixed or previously unseen degradations.
data-driven estimator of the
This probabilistic and ill-posed nature of IR also explains why different architectural paradigms may exhibit distinct reconstruction behaviors, depending on how degradation information and prior knowledge are encoded. To situate our design within the broader landscape of All-in-One IR, we first revisit several representative architectural paradigms that differ in their degree of parameter sharing, conditioning strategy, and task dependence. This comparison provides the conceptual motivation for our formulation and clarifies the design choices made our framework. As summarized in Fig. 1, these approaches can be interpreted as mappings from a degraded observation y to restored image R with different assumptions about how degradation type is modeled encoded in the network Jiang et al. (2025).
(a) m (1-1) Mapping. Each degradation type 2; E fl....,m} coder decoder pair (E,, D2) Li al. (2023a); Liang al. (2021):
m} handled by an individual en-
f (v)(
i= 1=
(2)
This strategy yields strong task specialization but scales linearly with the number of degradations and prevents knowledge sharing across tasks.
(b) m-1 Mapping with Shared Decoder. Multiple task-specific encoders are retained, while a shared decoder D reconstructs the output Li et al. (2020):
4 = D(E())), i= L....., m.
This partially amortizes parameters through common reconstruction head, yet the storage and computation of m encoders still limit efficiency and scalability.
(c) 1-1 Mapping with Prompts. A single encoder- pair (F,, D) is conditioned on external prompt p(z) describing the degradation Li et al. (2023c); Zhang et al. (2025); Li et al. (2023c):
f = D(E()),
(4)
where p(2) may correspond learned tokens or textual embeddings. This formulation improves flexibility and controllability across degradations, but introduces auxiliary conditioning modules and requires careful prompt modeling and tuning.
(d) 1-1 Mapping for All-in-One IR (adopted by our solution). We advocate a pure one-to-one mapping, where single incoder-decoder directly captures both degradatioon-sensitiee cues and ingradattioninvariant structures Cui et al. (2025); Tang et al. (2025):
a = D(E()))
5
Unlike prompt-based formulations, no explicit task tokens or routing mechanisms are introduced; instead, the network architecture designed to enable implicit disentanglement within the shared representation space, favoring parameter efficiency and generalization across degradations.
4

---

Published in Transactions on Machine Learning Research (02/2026)
Fb
Porch Finbvalingg
Refine DAR
Cony
x8 W
Ni
1st Ncale
M.
(2 Wxx
Skip Splii
Encoder DAB
Decoder DAB
deg
Ielean
Aftention
(iee 1.4)
Fw
A
N₂
Flam
x w/2 20)
CARLY
FPN
3rd Scale
Encoder DAB
[M/4
Decader DAB
(b) Degradation Adaption Block
Cony
Lager fiams
deg Input Samples
4th Scale
Latent DAB
Ielean Outpet Samples
Sue wwt.rggrmyyyyy
(H/8 W/88 8C)
Car
(a) Overall framework of the proposed AnylR
Figure 2: (a) Framework of the proposed AnylR: i.e., a convolutional patch embedding, a U-shape encoder- decoder main body, and an extra refined block. (b) Structure of degradation adaptation block (DAB).
This formulation-centric perspective highlights the tradc-offs between specialization, conditioning overhead, and parameter sharing in prior All-in-One IR systems, and motivates our design choice of a unified mapping that sooks to balance representational robustness with computational efficiency.
3.2 Overall Framework
Building on the design space reviewed in Sec. 3.1, now present our efficient All-in-One IR method, termed AnylR. As motivated by Fig. l(d), goal is realize pure 1-1 mapping that avoids multiple encoders or external prompts, while still capturing degraddttionsppecife cues and invariant structures within a single unified framework.
The overall architecture of AnylR is illustrated Fig. 2. At a macro level, it adopts a U-shaped network Ron- neberger et al. (2015) with four hicrarchical levels. Each level incorporates N;, E [1, 3, 4] instances of our proposed degradation adaptation block (DAB, Sec. 3.3), where each DAB is composed of the gated degradation adaptation module (GatedDA, Sec. 3.4) and the spatial spatial-frequency fusion algorithm (Sec. 3.5). Initially, convolutional layer extracts shallow features from the degraded input, creating patch embedding of size H x W x 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 lincar projection. Finally, a global skip connection links input to output, preserving high-frequency details and producing the restored image.
3.3 Degradation Adaptation Block
The proposed degradation adaptation block (DAB) serves as the fundamental unit of AnylR, and its structure is shown in Fig. 2(b). The design principle is to decouple global and local processing in parameter-offeient manner, while still retaining rich feature diversily. Given input feature Fin E @ xC we employ a selective channel-wise partitioning strategy:
Fatt = {F (2k-1)
KE Zz k5 g k Z k g)
(6)
Fgate in
(2A)
5

---

Published in Transactions on Machine Learning Research (02/2026)
DConv
Conv Linear
Conv Linear
F in
Nom
F
B
F.ll
Conv
Fgaae
ous
CONTROL
F
Fgated
Figure 3: Strueture of our GatedDA. @ c
@ and @ denote the element wise addition, channel-wist concatenation, GELU Hendrycks & Gimpel (2016) activation, and element wise multiplication, respectively,
where Fi and Firte e BR#xxx denote two interleaved channel groups. This skip-split partitioning is not intended to create two isolated feature branches; instead, it enforces controlled interaction across partial channel groups, so that degradation-varianl cues can be emphasized while degradation-ineen invariiii structural information remains accessible within the same shared representation space. This yields two complementary pathways: the attention branch Fintt focuses on long-range dependency modeling, while the gated branch peate spocializes in degradation-sonsee
local adaptation. Compared with conventional half-split operations, the skip-split design (i) reduces the effective dimensionality per branch, lowering the complexity of attention layers Dosovitskiy et al. (2020), and (ii) preserves feature diversity by uniformly sampling channels, thereby avoiding information loss. A detailed analysis is provided in Sec. 5, and the visual illustration is shown in the appendix.
To capture the complex dependencies inherent to global degradation, we employ a multi-depth convolution head attention mechanism Zamir et al. (2022); Potlapalli et al. (2024) on the feature subset Lms, resulting in Fout. This attention approach is particularly effective for image restoration, where degradation is often non-uniform and shaped by various intricate factors. Specifically, Fat is transformed into Query (Q), Key (K), and Value (V) matrices, defined as: Q = -uut W qry; K = <===aa Fatt key, V E W val, where W Ways W Wkey; key: and W val are learnable weights. The output Fatt is then calculated as:
QK
Fatt Σ Softmax
Vi,5
vd
where vd is a scaling factor to normalize attention scores, stabilize gradients, and facilitate convergence. This attention mechanism excels at modeling long-range dependencies throughout the feature space, a vital capability for image restoration where degradation, such as noise, blur, and artifacts, exhibits spatial correlations but varies across the image Liang et al. (2021); Zamir et al. (2022); Li et al. (2023a).
In parallel, the gated branch Egate is processed by the GatedDA module (Sec. 3.4), yielding pgate This path complements attention by enhancing localized, degradation--wwar features Liang et al. (2021). Thus, the DAB combines the strengths of global modeling (attention) and local selectivity (gated convolution).
To unify both pathways, we adopt a opatial-frequene fusion strategy (Alg. 2), producing the fused repre- sentation pluse Finally, layer normalization, a feed-forward network (FFN), and residual connection are applied:
Fout FFN(Norm((huse) + Fruse.
This sequence stabilizes feature statistics and enhances expressivity, yiclding a balanced representation that integrates global context with fine-grained degradation cues for high-fidelity restoration.
3.4 Gated Degradation Adaption
To capture local degradation-awar details, we leverage the selective properties of gated convolution, forming the GatedDA (Fig. 3). Given input feature pgate HxC layer normalization is first applied

---

Published in Transactions on Machine Learning Research (02/2026)
Algorithm 1 Gated Degradation Adaption
Require: Input Agate= E RR Xx initial temperature т
Ensure: Output ponte j2xxxx
F Norm Fggate
// Normalize // per-channel mean
2: 1 HW I E w= Fighha
a HW Ii=1 Ew=1 (F...hye - 11)2 +
per-channel std, 6: small constant
4: A + Sigmoid ju +0)
// Temp adjust
5: Tadj + A
6: F" - FW txp
// Channel expand
7: 7, 1,3,a++ + split(FF)) spplt(()))
///Spli 7, 3, a
8: a' t (aa (1 + Tadj)
//Depthwise conv
9: Faated + o7)) concat a') W
// Gate combine
gate
10: pgate
out + (Feated + +imat W proj
// Residual, proj
Fgate
11: return out
to stabilize the feature distributions, followed by 1x1 convolution that expands the channel dimension to hidden = Texpan C, where Texpan is the expansion ratio, as shown in Alg. 1. To adaptively respond to varying intensities, we introduce a temperature adjustment mechanism. Based on the input's mean and slandard deviation, the initial temperature т is modulated as Tadj F A, where A serves as a dynamic scaling factor (steps 2-5 of Alg. 1). This design is motivated by the fact that many degradations exhibit spatially non-uniform charactoristies; therefore, rather than applying a single global transformation, the gatod module performs content- and region-aware modulation on features, enabling localized adaplation to different. degradation strengths. This adjustment improves the model's sensitivity to subtle degradation patterns, promoting detailed feature capture.
The expanded feature F' (Wexp exp denotes the 1x1 convolution weights used for channel expansion) is then split into three parts along channels: a, 8, and 7 (as shown in step 7, which correspond to the scaled, ego, and shifted features). Specifically, we take (hidden/ss, hidden/4,, hidden/2) for (a,, B, 7) in this paper. Here, a undergoes depthwise convolution with Tadj to capture spatial details, 3 retains the original information, and y is activated with GELU Hendrycks & Gimpel (2016) enable a. non-linear gated selection mechanism. Specifically, these components are recombined and modulated, selectively emphasizing critical fcatures (Step 9 in Alg. 1). Finally, F" is projected back to the original channel dimension and combined with pgate through a skip connection to prevent loss of information. A final 1x1 convolution fuses the degradation-adappee features with the input, resulting in the output Fgate (Stops 10-11 in Alg. 1). Please note that W Wdepth: W Watte and W Prooj denote the learnable convolution weights for wepth-wise convolution, gated convolution, and projection convolution operations.
Owing to its design, GatedDA dynamically adjusts its internal temperature and gating behavior, cnabling the network to capture degradation-awaar features adaptively. This makes it a natural complement to the global attention branch, providing localized detail enhancement and stronger robustness to spatially varying degradations. More analysis is provided in the appendix.
From an efficiency perspective, the proposed design improves computational economy at the architectural level rather than relying on model scaling. First, each Degradation Adaptation Block partitions the feature channels into two subsets, where only half of the channels are processed by global attention. Since our attention operator follows the Restormer-style channel-wise formulation whose complexity grows quadratically with spatial resolution, reducing the attention channels by half directly lowers the computational cost of the attention pathway while presorving its ability to model long-range dependencics. Second, the remaining channels are processed by the proposed GatedDA module, which enhances the represenlational capacity of the block through lightweight, convolution- based local adaptation; this branch has substantially lower complexity than applying attention on the same number of channels, further contributing to overall efficiency. Finally, because each block combines a stronger per-block representation with a more balanced global-local decomposition, the backbone be instantiated with a smaller U-shaped hierarchy (e.g., [3, 5, 5, 7] instead
7

---

Published in Transactions on Machine Learning Research (02/2026)
Algorithm Spatial-Prequency Fusion
Require: Fatt: gatt: fusion weight A
Ensure: Fused output Fluse
Main Procedure:
1: F. < SPATIALFUSION Faato)
// spatial cross-enhance detail-enhance weighted fusion
Fy FREQUENCYFUSION
gate))
//
Fuse F. FF
return Fluse
Function: SpatialFusion
function SPATIALF USION Fatt; Saab;FFaate Fgaat)
1 +- Pat + Sigmoid( (att))
// g/gteeenhanced attention aatentiinnennaaneee enhanced gate // channel-wise merge
7: FE + Fgate + Sigmoid( Fass)
8: return Concat (Fs,, FEa)
9: end function
Function: FrequencyFusion
10: function FREQUENCYFFSSONN/ Fgate)
11: Patt + rrlltnn Fatt); Peare + rlf12n gate)
/// real FFT // ffreq combine /// inverse FFT // match channels
12: ++ Fatt FRato
13: Ftreq + -irfft2((F)
14: return Repeat(Fired)
15:
end function
of the deeper [4, 6, 8] configurations used in prior All-in-One IR methods such as PromptIR Potlapalli et al. (2024) and MoCE-IIR Zamfir et al. (2025)), while still achieving superior restoration accuracy.
3.5 Fpatual-Frequency Fusion Algorithm
To enable effective interaction between contextualized global attention and degradattion-sensttie local GatedDA features, we introduce a spatial frequency fusion algorithm (that is, Alg, 2). The fusion is carried out in two complementary domains: spatial and frequency.
In the spatial branch, we apply cross-enhancoment mechanism (i.e., SpationFusion in Alg. 2) to refine the attention feature Fatt and gated feature Fgate mutually. Each branch is modulated by the sigmoid-activated signal from the other, enabling a dynamic message passing across representations. The enhanced features are then concatenated along the channel dimension to form the spatial representation.
In parallel, perform lightweight drmainn fusion to capture structural alignment. Fau and F Fgate are first transformed using real-valued 2D Fast Fourier Transform (i.e., rf]t2p(*)) in Step-11 of Alg. 2, additively combined in the frequency domain (Step-12), and then projected back via inverse FFT (i.e., irfl)2(()) (Step-13). The output is duplicated (i.e., Repeat(*)) along the channel to match the spatial counterpart (Step-14). This design leverages the complementary roles of the two domains: the spatial branch emphasizes local structural fidelity, whereas the frequency branch stabilizes global statistics and degradation patterns, which is particularly beneficial under mixed compound degradations.
The final fused representation is obtained by weighted summation of the two branches (Step-3 of Alg, 2), controlled by scalar A. A convolutional projection with learnable weights W is then applicd, followed by a residual connection to the original input:
F phuse Convw (Fruse) Fin-
This fusion strategy leverages the complementary strengths attention- baaettdo--baedd global context and gated local priors from both spatial and frequency domains, producing rich and adaptive representation for downstream restoration. Notably, the design aligns with signal-domain interpretability while enhancing generalization across diverse degradation types.
8

---

Published in Transactions on Machine Learning Research (02/2020)
Dagradation Performanca
Deggrddation Performance [Ave]
Emelency Comparaonn
3215
1071
Xn
0:00
30.55
(390
MaCE
9:15
2013
RAEEA (OWP)
2455
690
2942
(a) The 3-Deg. Performance Comparison
(b) The 5-Deg. Perfornance Comparison
(c) The efficiency Comparison
Noise
PrompeIR
lnput
PromptIR
Hazy Input
PrompiR
Ground Truth
AnyIR (Ours)
Ground Truth
In (Ours)
Ground Truth
Any (Ouns)
(d) Visual Comparison the 3-Deg, setting denoising, draining, and dehazing (Zoom in for better view).
Figure 4: Overall quantitative performance (3- "3-Deraadttion & 5-Degradation settings), efficiency comparison, and the qualitative comparison Zoom in for better view).
4 Experimental Results
We conduct experiments adhering to the protocols of previous general TR works Potlapalli et al. (2024); Zhang et al. (2023) in three main settings: (1) All-in-One (3Degrations), (2) All-in-One (5Degrations). (3) Mix- DeggDdegraotonn Setting, and (4) Zero-Shot Unseen Setting More experimental details and the introduction of the data set are provided in our Supp. Mat.
Before presenting the detailed results for each setting, first provide an overview comparison to summarize the overall performance, efficiency, and visual restoration quality of AnylR against, representative All-in-one IR methods, shown in Fig. 4.
4.1 State of the Art Comparisons
Three Degradations. We evaluate our All-in-One restorer, AnylR, against other specialized methods listed in Tab. 1, all trained on three degradations: dehazing, deraining, and denoising. AnylR consistently outperforms all the comparison methods, even for those with the assistance of language, multi-task, prompts. In particular, AnylR outperforms the baseline method PromptIR by 1.12dB, 2.14 on dehazing and draining, and 0.74dB on average, while maintaining 80% fewer parameters.
Five Degradations. Extending the three degradation tasks to include deblurring and low-light enhance- ment Li et al. (2022); Zhang et (2023), we validate the comprehensive performance of our method in an All-in-One sctting. As shown in Tab. 2, AnyIR effectively leverages degradation-specife fcatures, surpassing AirNet Li et al. (2022) and IDR Zhang et al. (2023) by an average of 5.16 dB and 2.31 dB, respectively, with 33% and 60% fewer parameters. Compared to the most recent method, MoCE-IR Zamfir et al. (2025), besides 0.05 dB lower on deblurring, we outperform it on all the rest tasks with 0.57dB PSNR improvement average.
Mixed Degradation. We conduct experiments on the CDD-11 Guo et al. (2024b) dataset, a challenging benchmark for mixed degradation restoration tasks, combining real-world degradations such as low-light conditions, haze, rain, and snow. As shown Tab. 3, our method consistently surpasses other state-of the-art approaches like AirNet Li et. al. (2022), PromptIR Potlapalli et al. (2024), WGWSNet Zhu et al. (2023),
9

---

Published in Transactions on Machine Learning Research (02/2026)
Table 1: Comparison to state-of-the-art on three degradations. PSNR (dB, t) and SSIM (t) metrics are reported on the full RGB images. Best and Second Best performances is highlighted. Our method sets a new state-of-thee ahe-art on average across all benchmarks while being significantly more efficient than prior work. '' reprosents unreported results.
Dekazing SOTS
Devaining
Denoising
Method
Venue.
Params.
Average
Rain100L
BSD68
BSD
BSD68
BRDNet Tlan et sl. (2020)
NN'20
23.23
895
27,
895
32
26
898
29.76
.866
26.34
093
27
87.80
.843
L.Net Gao et al. (2019)
CVPR119
20.84
.888
24.88
.784
26.47
.778
24.77
.748
21.26
.552
23.64
.738
FDGAN Dong et al. (2020)
AAAT20
24.71
.209 29.89
933
30.25
910
28.81
.868
26.43
.776
28.02 .883
DL Fan et al. (2019)
1
2M
26.92
991
32.62
1911
33.05
.94
30.41
.861
26.90
.740
29.98
.876
MPRNet Zamir
al. (2021)
CVPR221
16M
25.28
.955
33.57
.54
33.54
.927
30.89
.880
27.56
.779
30.17 .890
AirNet Li et al. (2022)
CVPR'22
9M
27.94
.962 34.90
.967
33.92
.93
31.26
.888
28.00
.797
31.20
.910
NDR Yao et al. (2024)
TIP'24
48.4M
25.01
860
28.62
8488
28.72
826
27.88
.798
26.18
.720
25.01 .810
PromptlR Potlapalli et al. (2024)
36M
30.58
.94
36.37
.72
33.98
.933
31.31
.888
28.06
.799
32.06 .913
MoCE-IR-S Zamfir et al. (2025)
CVPR'25
11M
30.98
.979
38.22
.983
34.08
.933
31.42
.888
18.16
.798
32.57
.916
AnylR -(rrr)) AnyIR -S(Our))
2025
6M
31.70
.982
38.51
.983
34.12
.936
31.46
.893
28.20
.804
32.80
.919
2025
9M
31.85
.982 38.56 .983 34.15 .936
31.49 .893 28.24
.806
32.86 .920
Methods with assistance of vision language, multi-task learning, natural language prompts, or multi-modal control
DA-CLIP Luo et al. (2024)
ICLIU24
125M
29.46 .963 36.28 9968 30.02 .821 24.86 .585
22.29 .476
AttprompsSR R Wu et al. (2021a)
ACM M1
36M
30.83
.979 37.94 .82 34.06
.94
31.42
8811
28. 14 801
32.49 .917
InstructIR-3D Conde et al. (2024)
ECCV'21
16M
30.22
.959 37.98
.978 34.13 .93
31.52
.80
28.30
804
32.43 .913
UniProcessor Duan al. (2025)
ECCV'24
1002M
31.66
.979
38.17
.982
34.08
.935
31.42
.891
28.17
8803
32.70
.918
Table 2: Comparison to state-of-the-art on five degradations. PSNR (dB, 1) and SSIM (t) metrics are reported on the full RGB images with (*) denoting general image restorers, others are specialized all-in-one approaches. Best and Second Best performances is highlighted.
Dehazing
Deraining Denoising Debiarring Low- Loghtight
Mothod
Venue
Params.
Average
SOTS
Rain100L BSD68
GoPro
LOLv1
NATNet" Chen al. (2022)
ECCV'22
17M
25.23
.939 35.56 .97 31.02
883
26.53
888
20.49 .809
27.76
.881
DGUNet" Mou et al. (2022)
CVPR'22
17M
24.78
940
36.62
971
31.10
883
27
25
837
21.87
823
28.32
.891
SwinlR" Liong et al. (2021)
ICCVW"21
21.50
801
30.78
923
30.39
24.52
773
17.81
.723
25.04
.835
Restormer' Zamir al. (2022)
CVPR'22
26M
24.09
.927
31.81
962
31.49
884
27.22
829
20.41
806
27.60
.881
MambalR' Guo et al. (2024a)
ECCV'24
27M
25.81
.944
36.55
971
31.41
.84
28.61
.875
22.49
.82
28.97
.901
DL. Fan et al. (2019)
MPAMF19
2M
20.54
.82
21.96
.762
23.09
.745
19.86
.672
19.83
.712
21.05
.743
Trans Traus Weather Valanarasu et al. (2022)
CVPR'22
38M
21.32
888
29.43
.90
29.00
841
22.12
.757
21.21
.792
25.22
.836
TAPE Liu al. (2022)
ECCV'22
1M
22.16
861
29.67
.901
30.18
855
24.47
763
18.
621
25.09
.801
AirNet Li et al. (2022)
CVPR'22
9M
21.04
884
32.98
951
30.91
882
24.35
.781
18.18
.735
25.49
.817
IDR Zhang et sl. (2023)
CVPR'23
15M
25.24
994 35.63
.96
31.60
.887
27. 87
846
21.34
882
28.34
.893
PromptIR Potlapalli et al. (2024)
NeurIPS'23
36M
30.41
.972 36.17
970
31.20
.885
27.93
8851
22.89
.829
29.72
.901
MoCE-IR-S Zamfir et al. (2025)
CVPR'25
1IM
31.33
.98
37.21
.98
31.25
.844
28.90
.877
21.68
.851
30.08
.913
AnyIR -T (ours)
2025
6M
31.50
.981
38.81
.984
31.40
.892
28. 35
863
22.68
.854
30.71 .915
AnylR- - (ours)
2025
9M
31.77
.982 39.00 .983
31.44 .892
28.52
.867
23.03
.857
30.75
.916
Methods with the assistance of natural language prompts or multi-task learning
InstructIR-5D Conde et al. (2024) ArtPromppiR Wu et al. (2024a)
ECCV'24
16M
36.84
.973 27.10 .956 31.40 .887 29.40 .886 23.00 .836 29.55 .908
ACM MM'24
36M
29.93
.908 22.09
.891 29.43
.843 25.61
.776 21.99 .811 25.81 .846
WeatherDiff Özdenizei & Legenstein (2023), OneRestore Guo et al. (2024b), and MoCE-IR Zamfir et al. (2025). These results demonstrate its robustness in addressing complex interactions among degradations. The superior performance of AnyIR validates its advanced degradation modcling and fusion mechanisms, enabling effective restoration in various scenarios.
Zero-Shot Unseen Degradation. To assess generalization beyond training degradations, we first evaluate the model trained on the 3-degradation setting directly on the unseen desnowing task (i.e., zero-shot transfer) using the CSD dataset Chen et al. (2021a). As shown in Tab. 4, AnyIR extends effectively to this novel degradation without domain-specific tuning. We further conduct a real-world zero-shot evaluation on underwater images. AnylR -S achieves 16.78 dB PSNR and 0.770 SSTM, outperforming the best prior method MoCE-IR by +0.87 dB while being more compact (Tab. 5). Notably, our model has never seen underwater data during training, underscoring its robustness to unseen degradation scenarios.
10

---

Published in Transactions on Machine Learning Research (02/2026)
Table 3: Comparison to stale-of-lhe-arl on composited degradations. PSNR (dB, f) and SSIM (t) are reported on the full RGB images. Our method consistently outperforms even larger models, with favorable results in composited degradation scenarios.
Method
Parann.
CDD1 S-Sgle
CODLI-DesMle
CODTL Triple
Ara.
Low
Haze
(R)
Snow (8)
L++H
L++R
L-S
1++
++++++
L+
AizNet
9M6
24.83
.778
24:21
961
28
.891
20.79
919
28
28
779
22.3
.710
23.29
723
22.21
.988
25:29
501
21.80 10
708
22:24
.726
28.75
.114
Prompelft
36M
56.32
26 10
868
31.
11
9916
31-33
21 49
789
25.05
24.51
21
924
23-
825
782
23.33
747
25
850
WNet
2634
24.38
80
882
33
964
34.43
873
27
800
25
24
27 23
955
27
23.97
771
863
WeathrrDii
633
25.58
768
21 99
904
26.85
885
24.
21 83
736
32
730
22
21
888
21 51
6988
799
OucRestore
6M
25.19
957
30.
791
28.
MoUB-IRR
11M
27:20
65
34.31
33.91
96
30
AnyIR-T(owre
6M
27.40
1833
33:41
891
34 53
9970
06.07
877
26 53
1827
26 55
1810 26.36
802 30
1963
30.65
1970
25 05
800
25.
(801
29. 40 886
AnyTR-9(mung)
10M
27.47
8835
34.38
.992
(44.04
.971 (6621
(981) 26.54
1800 26.48
2 26.45
.805 .8053805500000 30.98 .967 (11 .55 .972 25
.804
26.01
.805
29.09 .889
Table
Unseen Desnowing on CSD Chen el al. (2021a) dataset.
Method
PSNR
SSIM
AirNet Li (2022)
19.32
.733
PromptIR Potlapalli et al. (2024)
20.47
.764
MocelR-S Zamfir ct al. (2025)
21.09
.771
AnylR (Ours)
21.64
.787
Table 5: Zero-Shot Moos--Domain Underwater Image Enhancement Results.
Method
PSNR (dB, 1)
SSIM (t)
Swinlit Liang at al. (2021)
15.31
.740
NAFNet Clu el al. (2022)
15.42
.744
Restormer Zamir
(2022)
15.46
.745
AirNet et al. (2022)
15.46
.745
IDR Zhang sl. (2023)
15.58
.762
PromptIR Potlapalli et al. (2024)
15.48
.748
MoCE-IR Zamfir et al. (2025)
15.91
.765
AnyIR -S (Ours)
16.78
.770
Table 6: Complexity Analysis. FTOOPs are computed on image size 224 x 224 via NVIDIA A100 (40G) GPU.
Method
PSNR (dB, f)
Memory (J)
Params. (1)
FLOPs (L)
AirNet et al. (2022)
31.20
4829M
8.93M
238G
PromptIR Potlapalli et al. (2024)
32.06
9830M
35.50M
132G
IDR Zhang et al. (2023)
4905M
15.34M
98G
MoCE-II Zamfir et al. (2025)
32.73
5887M
25.35M
80.59 t 5.21G
MoCE-IR-S Zamfir et al. (2025)
32.57
4228M
11.47M
36.93 + 2.32G
AnylR- -T(our))
32.80
4969M
5.74M
26 + 1.98G
AnyTR -S(ours)
32.86
6661
8.51M
39 + 1.87G
Model efficiency. Tab. 6 compares memory usage, FTOPPs, and parameters across recent All-in-One IR. methods. With our hybrid block design and the proposed degradation adaptation module, AnylR achieves a 0.74 dB PSNR gain over the baseline PromptIR. Potlapalli et al. (2024), while reducing parameters by 83.9%, and FLOPs only 26G, making it 80% more computationally efficient. Compared with MoCF-IR-S, AnyIR lowers FLOPs by 29.73% (26G vs. 37G) and paramcters by 49.96% (5.74M vs. 11.47M), while maintaining comparable or superior accuracy. Such reductions not only improve elficiency in terms of model size and computation, but also translate into smaller energy and compute footprint during inference, which is particularly relevant for resource-constrained or edge deployment. This positions AnyTR as a strong and sustainable baseline for future All-in-One IR research.
Visual results. To complement the quantitative results, we visualize the results of our method in Fig. 5. The visualizations demonstrate the efficacy of AnylR in dehazing, denoising, and draining, and we marked out the detailed region using the red boxes. In the dehazing task, AirNet Li et al. (2022), PromptIR Potlapalli et al. (2024), and MoCF--IR Zamfir et al. (2025) exhibit limitations in fully eliminating haziness, leading
11

---

Published in Transactions on Machine Learning Research (02/2020)
Degraded Input
AitEet
PromptiR
MoCE-IR-S
AaylR (Ours)
Gruuddruth
Denoising (21077)
Doraing (015)
Deloczing (9409)
Figure 5: Visual comparison on three degradations. Zoom in for better view,
Jan Ang
Azyll 1aag
Befocsss Iag
Baw 201
Axy (Dumg)
Rekeeace Iny
Figure 6: Zero-Shot Underwater Image Enhancement Visual Results. Zoom for a better view.
noticeable color reconstruction discrepancies. In contrast, our AnylR effectively enhances visibility and ensures precise color reconstruction. The denoising results also show that AnyIR restore more detailed characters, demonstrating the rich texture edge recovery ability of our method. Meanwhile, in rainy scenes, previous methods continue to exhibit remnants of rain streaks. Please, zoom in for more details. In contrast, our approach excels at climinating these artifacts and recovering underlying details, showing our superiority under adverse weather conditions. Note that the PromptIR is approximately 6x larger than ours. Despite this, our method consistently produces visually superior results, demonstrating its effectiveness. Fig. 6 also shows that under the zero-shot setting, our method can also restore clear results. More detailed visual examples are given in the appendix.
4.2 Ablation Studies
Impact of different components. We conduct detailed studies on the proposed components within the framework of AnylR All experiments are conducted in the All-in-Owe One setting with three degradations. We compare our simple feature-modeling block DAB against other variants. As detailed Tab. 7, we assess the effectiveness of our key architectural contributions by removing or replacing our designed module with its counterparts. The detailed architccture overview of these considered counterparts can be found Fig. 7.
We first examine the impact of our skip-split operation (a-b), which yields significant improvement of 1.02 dB over the common half-split method. This validates and supports our motivation to deeply enable intertwining within the subparts of an input feature. Introducing our proposed GatedDA in parallel to the attention layer (c) results in a substantial increase of 1.27 dB. Combining GaledDA with the skip-split operation (d) further enhances the reconstruction fidelity of our framework. This validates the effectiveness of our intention of using local gated details to reconstruct the degradation-await output. Lastly, the introduction of cross-feature filtering (e), please refer to Fig. 2 for our plain design, improves the interconnectivity between global-local features. further benefiting overall performance.
12

---

Published in Transactions on Machine Learning Research (02/2020)
F
Fin
Fjn
F
in
(R/C,
6,
(.
half-oplit
Sleip-split
half-oplit
Skip-split
(8 M.0)
C
(ng (...)
Attention
GatedDAA
Attention
GatedDAA
Attention
Atlention
Conv
Conv
FFN
FFN
FFN
FFN
Fout
F
Fout
Fout
(n)
(b)
(d)
Figure Structure of other different DAB variants during exploration.
Table 7: Ablation comparison (Average PSNR) of the effect of cach component under the 3-degradation.
Method
Skip-Split
Fusion (Alg. GatedDA
PSNR (dB,
(a)
X
X
31.83 X
30.85
(b)
32.13
(d)
32.35
(c)
32.80
Table Impact of different (a, 3, 7) sottings on thrco-degradation.
(a, 9, 7)
PSNR f
SSIM f
(0, 1/2, 1/2)
32.14
0.910
(1/2, 0, 1/2)
32.21
0.912
(1/2, 1/2, 0)
31.37
0.907
(1/4, 1/4, 1/2)
32.80
0.919
rain-97
Figure 8: Visual feature maps of a, B, and within GatedDA.
Impact of different a, 8, and y in gatedDA. Tab.8 shows the different impact of different values of (a, 8, 7) in the gatedDA proposed (without Alg. 2). We noticed that when setting one of these parameters to 0, the performance decreases. When we set (a, 33 7) to (hidden/s, hidden/s, hidden/2), the average performance increases. This means that cach part of the proposed gatedDA is necessary. The visual features shown in Fig. 8 indicate that a, 3, and y consistently focus on various aspects of the degraded regions, each specializing in different levels or types of degradation. See more analyses in our Supp. Mal.
Impact of the network capacity, The results Tab. 9, derived from experiments on the draining task, provide valuable insight into the broader context of all-in-one IR. Although draining serves as testbed, the findings reflect key question in general-purpose restoration: Does increasing model capacity unéversally
13

---

Published in Transactions on Machine Learning Research (02/2020)
300
(b) Hall Split Activation
2nd Hpli- Activation
(d) Original Activation
(c) Even Skip-Split Activation
(1 Odd Skip-Split Activation
Figure Visualization of the channel activation distribution.
Table 9: Network Capacity and Complerity Analysis on Deraining. Comparison of PSNR, model paramcters, and FLOPs, highlighting the balance between network capacity and computational efficiency. FLOPs are computed for 224 x 224 input images using an NVIDIA Tesla A100 (40G) GPU.
Method
Blocks
PSNR (dB, 7)
Params. (+) FLOPs (L)
PromptIR Potlapalli et al. (2024)Basee)
[4,6,688
37.04
35.6M
132G
Base (No Prompt))
[46,6688
36.74
32.2M
117G
Base (Fix-Prommpt)
[4,6,6,8
36.85
Base (No Prompt)
[,5,5,7]
36.84
29. 1M
101G
Base (No Prompt)
[2,4,4,6]
36.76
26.0M
85C
Base (No Prompt)
[1,3,3,]]
36.63
22.9M
68G
AnyTR (ours)
[3,5,577]
37.99
5.74M
26G
lead to better performance? Our analysis suggests otherwise, emphasizing the importance of efficiency and lask-aware design over brute-force scaling.
For example, the base PromptiR Potlapalli et al. (2024) model with blocks [4,6,6,8] achieves a PSNR of 37.04 dB through 35.6M parameters and 132G FLOP, and progressively reducing capacity through configurations such as [3,5,5,7], [2,4,4,6] and [1,3,3,5] results smaller models with lower computational costs but marginal decreases PSNR. This highlights the diminishing returns of simply reducing complexity without optimization, smaller models lose their capacity to capture the mannces of degradation factors. In contrast, AnyIR validates that careful balance between network capacity and architectural innovation results. With a similar block configuration (3,5,,577 but a significantly optimized architecture, AnyTR achieves a PSNR of 37.99 dB on the deraining task, outperforming larger models while using only 5.74M parameters and 26G FLOPs. This demonstrates the offectiveness our task-specific improvements and highlights the patential to achieve better results with smaller, more efficient models. These findings underscore critical insight for all-in-onc IR: While larger models may generalize better across diverse tasks, efficiency and lailored design can lead to both higher performance and practical utilily. The deraining results provide compelling argument for rethinking model capacity in restoration frameworks, emphasizing that "more" is not always better, especially when thoughtful design can yield both performance gains and computational efficiency.
achieve SOTA
Exploration of À value in Spatual-Frequency Fusion. module: spatial-only, frequency-only, fixed, and learnable. As shown in Tab. 10, the learnable stratogy yiclds the best results, highlighting the benefit of adaptive spatial-frequency balancing. Moreover, both fixed and learnable schemes surpass single-branch counterparts, confirming the complementarity of the two domains.
We explore different settings in the fusion
14

---

Published in Transactions on Machine Learning Research (02/2026)
Table 10: Ablation comparison of different fusion strategies.
Fusion
PSNR (dR, t)
SSTM (t)
Spatial-only
1.0
32.37
.915
Prequency-only
0.0
32.09
.919
Fixed (0.5)
0.5
32.63
.917
Learnable
32.80
.919
5 Discussion
What does the proposed Skip-Split bring? Adjacent channels often contain redundant information due to spatial correlations in the data. Directly splitting the channels into two contiguous halves can lead to uneven feature distribution, with one half potentially capturing redundant features, while the other lacks important information. By using a simple skip-split method that interleaves channels between the two processing paths, we ensure a more balanced and diverse set of features in each path, enhancing the effectiveness of both the self-attention and convolutional components. These phenomena are also validated in the channel activation distribution visualization shown in Fig.
Why does the proposed GatedDA & the fusion Alg. 2 work? As shown in Fig. 10, the error map visualizations reveal that degradation in an input image is often unevenly distributed, manifosting itself as global patterns and localized clusters. This highlights the need for degradation modeling to capture both widespread and fine-grained distortions. The proposed GatedDA module addresses this by selectively activating in degraded regions, closely aligning with the actual degradation distribution, and effectively enhancing localized features. When combined with global attention, which captures broader contextual dependencies, the fusion algorithm (Alg. 2) enables more comprehensive understanding of the degradation structure of the image.
Further evidence is provided by the SVD and cumulative variance curves in Fig. 11, which demonstrate the complementary nature of the two modules. While the atlention branch captures dominant global variations reflected in a steep variance accumulation and concentrated singular values, GatedDA captures more diverse and spatially distributed local signals. The fused representation achieves a better balance, integrating both local and global characteristics with faster variance accumulation than GatedDA alone. These results validate that the synergy between GatedDA and attention not only improves restoration quality but also enhances robustness across diverse types of degradation. Although some aspects of this analysis can be interpreted at more abstract represeniationnlleaniig level, our formulation and conclusions are confined to the image restoration setting, where the model is developed and evaluated specifically for multi-degradattinn IR.
Why is AnylR efficient? AnylR reduces computational costs by splitting input channels: one half is processed by self-attention, the other by a Gated CNN block. This division reduces the complexity of self-attention from O(B- head (H- - WW)), while Gated CNN processes the remaining channels with a lower complexity of O(B C. H W), especially beneficial for high-resolution inputs where (H W)² dominates. Furthermore, AnylR employs dimensionality reduction and parameter-efficien designs, collectively reducing the GFLOPss and model parameters. This balance of global context modeling and efficient local feature extraction enables AnylR to minimize computational costs without sacrificing performance.
Is scaling down the new advantage? Although recent methods in image restoration often scale up model size and complexity, our AnylR takes different path: scaling down. Instead of relying on large architectures, we embrace a simple but non-trivial design, using targeted components like the GatedDA module to capture degradation without excessive parameters effectively. This simplicity is not compromise; it is asset yielding high-quality restoration with minimal computational demand, faster training, and greater adaptability. The All-in-One IR framework, though new compared to dogradation-speecfic methods, faces a key limitation: an imbalance in data distribution, with certain degradations (e.g. dehazing) dominating, Our experiments suggest that a more balanced dataset could significantly improve performance across degradation types, offering a constructive direction for future work. We note that these discussions and observations are made strictly within the scope of multi-degrradation image restoration, and are not intended to imply a general-purpose image processing or prediction framework beyond the IR domain.
15

---

Published in Transactions on Machine Learning Research (02/2026)
Error Map
Feature Gated
Input
AnylR Output
Dehozing
304
200
300
4.0
300
Denoising
Deraining
100
209
Figure 10: Error map and the output of GatedDA
Deparied
W0th
Comminan SV11 (atw
Cheed lve Witchend
Chomal AV
Figure 11: SVD and cumulative variance curves for attention, GatedDA, and the fused feature (Zoom in for better view).
Effect of degradation distribution. We further observe that commonly used megliddegradation IR training sets (3 Degradation) tend to exhibit an imbalanced composition across degradation types. By making the distribution more uniform (Fig. 14) under the same 3-degradation setting without increasing the total number of samples- we consistently obtain improved restoration performance (Tab. 12). This trend suggests that the learned reconstruction prior is influenced by the training degradation distribution, and a more balanced composition can lead more stable and gencralizod restoration behavior.
6 Conclusion
We introduced AnyIR, efficient multi-degradattin image restoration model that unifies the degradations considered in current All-in-One IR settings within single framework. By combining gated guidance with spatial-frequency fusion strategy, the model learns embeddings that capture both degradatiin-spceifee cues and invariant utrs enabling robust restoration across lasks. Extensive experiments, including evaluations on unseen and real-world degradations, show that AnylR achieves satte-of-the-ate accuracy with substantially reduced parameters and FLOPs, while maintaining strong generalization ability. We believe that AnylR provides strong and efficient baseline for future rescarch, advancing both the practical deployment and the broader understanding of learning-based all-in-one image restoration.
16

---

Published in Transactions on Machine Learning Research (02/2026)
Acknowledgments
This work was partially supported by the FIS project GUIDANCE (Debugging Computer Vision Models via Controlled Cross-modal Generation) (No. FIS2023-03251), the Alexander von Humboldt Foundation, and the National Natural Science Foundation of China (624033455..
References
Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik. Contour detection and hierarchical image segmentation. IEEE TPAMI, 33(5):898-916, 2010. 22
Mark R Banham and Aggelos K Katsaggelos. Digital image restoration. IEEE Signal Processing Magazine, 14(2):24-41, 1997. 2
Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. Simple baselines for image restoration. In ECCV, pp. 17 33, 2022. 2, 10
Wei- Ting Chen, Hao- Yu Fang, Cheng-Lin Hsieh, Cheng-Che Tsai, I Chen, Jian-Jiun Ding, Sy-Yen Yen Kuo, et al. All snow removed: Single image desnowing algorithm using hicrarchical dual-tree complex wavelet representation and contradict channel loss. In ICCV, pp. 4196 4196-4205, 2021a. 10, 11
Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit 2021b.
image function. In CVPR, pp.
Xiaojic Chu, Liangyu Chen, and Wenqing Yu. Nafssr: Sterco image super-rcsolution using nafnet. In CVPR, pp. 1239 1248, June 2022. 11
Marcos Conde, Gregor Geigle, and Radu Timofte. Instructir: High-quality image restoration following human instructions. In ECCV, 2024. 2, 10
Yuning Cui, Syed Waqus Zamir, Salman Khan, Alois Knoll, Mubarak Shah, and Fahad Shahbaz Khan. Adair: Adaptive all-in-one image restoration via frequency mining and modulation. ICLR, 2025. 3,
Tri Dao and Albert Gu. Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality. In TCML, 2024. 3
Xiaohuan Ding, Yangrui Gong, Tianyi Shi, Zihang Huang, Gangwei Xu, and Xin Yang. Masked snake attention for fundus image restoration with vessel preservation. In ACM MM, pp. 4368 1368-4376, 4376, 2024.
Chao Dong, Yubin Deng, Chen Change Loy, and Xiaoou Tang. Compression artifacts reduction by deep convolutional network. Tn ICCV, pp. 576-584, 2015. 2
Yu Dong, Yihao Liu, He Zhang, Shifeng Chen, and Yu Qiao. Fd-gan: Generative adversarial networks with fusion-discriminatoo for single image dehazing, In AAAI, 2020. 10
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2020. 3, 6
Huiyu Duan, Xiongkuo Min, Sijing Wu, Wei Shen, and Guangtao Zhai. Uniprocessor: text-induced unified low-level image processor. In ECCV, pp. 180 199. Springer, 2025. 10
Akshay Dudhane, Omkar Thawakar, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming- Hsuan Yang. Dynamic pre-training: Towards efficient and scalable all-in-one image restoration. arXiv preprinl ar Xiv::2444 02154, 2024. 3
Qingnan Fan, Dongdong Chen, Lu Yuan, Gang Hua, Nenghai Yu, and Baoquan Chen. A general decoupled learning framework for parameterized image operators. TPAMI, 43(1):33 47, 2019. 10
Hongyun Gao, Xin Tao, Xiaoyong Shen, and Jiaya Jia. Dynamic scene deblurring with parameter selective sharing and nested skip connections. In CVPR, 2019. 10
17

---

Published in Transactions on Machine Learning Research (02/2026)
Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, and Baochang Zhang. Implicit diffusion models for continuous super-resolution. Tn CVPR, pp. 10021-10030, 2023. 2
Albert Gu and Tri Dao. Mamba: Linear-time sequence modeling with selective state spaces. In Language Modeling, 2024. 3
Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tap Xia. Mambair: A simple baseline for image restoration with state-space model. In ECCV, 2024a. 3, 10
Yu Guo, Yuan Gao, Yuxu Lu, Ryan Wen Liu, and Shengfeng He. Onerestore: A universal restoration
framework for composite degradation. In ECCV, 2024b. 9, 10, 22
Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08/ 08,11 2016. 6, 7
Jia-Bin Huang Abhishek Singh, and Narendra Ahuja. Single image super-rosolution from transformed self-exemplars. In CVPR, pp. 5197-5206, 2015. 22
Hu JiaKui, Zhengjian Yao, Jin Lujia, and Lu Yanye. Universal image restoration pre-training via degradation classification. In ICLR, 2025. 22
Junjun Jiang, Zengyuan Zuo, Gang Wu, Kui Jiang, and Xianming Liu. A survey on all-in-one image restoration: Taxonomy, evaluation and futurre trends. TPAMI, 2025. 3,
Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In CVPR, pp. 8346 8355, 2020. 3
Yitong Jiang, Zhaoyang Zhang, Tianfan Xue, and Jinwei Gu. Autodir: Automatic all-in-one image restoration with latent diffusion. arXie preprint arXiv:2310.10111 2023. 2
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 22
Lingshun Kong, Jiangxin Dong, Jianjun Ge, Mingqiang Li, and Jinshan Pan. Efficient frequency domain-based transformers for high-quality image deblurring In CVPR, pp. 5886-5895, 2023. 3
Wei-Sheng Lai, Jin Huang, Narendra Ahuja, and Ming- Hsuan Yang. Deep laplacian pyramid networks for fast and accurate super-resolution. In CVPR, pp. 624-632, 2017.
Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. IEEE TIP, (811)4492-503 2018. 22
Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, and Xi Peng. All-in-one image restoration for unknown corruption. In CVPR, pp. 17452 17462, 2022. 2, 3, 9, 10, 11,
Chongyi Li, Chunle Guo, Wenqi Ren, Runmin Cong, Junhui Hou, Sam Kwong, and Dacheng Tao. An underwater image enhancement benchmark dataset and beyond. TIP, 01-4376-4380 2019. 22
Ruoteng Li, Robby T Tan, and Loong-Fah Cheong. All in one bad weather removal using architectural search. In CVPR, pp. 3175-3185, 2020. 4
Yawei Li, Yuchen Fan, Xiaoyu Xiang, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Lue Van Gool. Efficient and explicit modelling of image hierarchies for image restoration. In CVPR, pp- 18278 18288, 2023a. 1, 2, 3, 4,
Yawei Li, Kai Zhang, Jingyun Liang, Jiczhang Cao, Ce Liu, Rui Gong, Yulun Zhang, Hao Tang, Yun Liu, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool, LSDIR: A large scale dataset for image restoration. In CVPRW, pp- 1775-1787, 2023b. 2
18

---

Published in Transactions on Machine Learning Research (02/2026)
Yawei Li, Bin Ren, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Nicu Sebe, Ming-Hsuan Yang, and Luca. Benini. Fractal-ir: A unified framework for efficient, and scalable image restoration. or arXie preprinl arXiv:2503. 17825, 2025. 1
Zilong Li, Yiming Lei, Chenglong Ma, Junping Zhang, and Hongming Shan. Prompt-in-prompt learning for universal image restoration. arXiv preprint arXiv:2312.00 050088, 2023c. 2, 3,
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. SwinlR: Image restoration using Swin transformer. In ICCVW, pp. 1833-1844, 2021. 2, 3, 4, 6, 10, 11
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super-resolution. CVPRW, pp. 1132-1140, 2017.
Chang Liu, Mengyi Zhao, Bin Ren, Mengyuan Liu, Nicu Sebe, et al. Spatio-temporal graph diffusion for text-driven human motion generation. In BMVC, pp. 722-729, 2023. 2
Lin Liu, Lingxi Xie, Xiaopeng Zhang, Shanxin Yuan, Xiangyu Chen, Wengang Zhou, Houqiang Li, and Qi Tian. Tape: Task-agnostic prior embedding for image restoration. In ECCV, 2022. 2, 10
Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölnd,, and Thomas B Schön. Image restoration with mean-reverting stochastic differential equations. arXiv preprint or Nin:2301 1 11699, 2023. 2
Ziwei Luo, Fredrik K Gustafisson, Zheng Zhao, Jens Sjölndd, and Thomas Schön. Controlling vision-language models for universal image restoration. In ICLR, 2024. 2, 10
Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang. Waterloo exploration database: New challenges for image quality assessment models. IEEE TIP, 26(2): 1004-1016, 2016. 22
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. database of human segmented natural images and its application to evaluating segmentation algorithms and mcasuring ecological statistics. In ICCV, pp. 416-423, 2001. 22
Yang Miao, Francis Engelmann, Olga Vysotska, Federico Tombari, Mare Pollefeys, and Dániel Béla Baráth. Scenegraphloc: Cross-modal coarse visual localization on 3d scene graphs. In ECCV, pp. 127-150. Springer, 2024.
Chong Mou, Qian Wang, and Jian Zhang. Deep generalized unfolding networks for image restoration. In CVPR, pp. 17399 17410, 2022. 10
Scungjun Nah, Tae Hyun Kim, and Kyoung Mu Lec. Deep multi-scale convolutional neural network for dynamic scene deblurring. In CVPR, pp. 0883-3891, 2017. 22
Ozan Özdenizci and Robert Legenstein. Restoring vision in adverse weather conditions with patch-based
denoising diffusion models. TPAMI, 45(8):10346 10357, 2023. 10
Vaishnav Potlapalli, Syed Waqas Zamir, Salman H Khan, and Fahad Shahbaz Khan. Promptir: Prompting for all-in-one image restoration. NeurIPS, 36, 2024. 2, 3, 6, 8, 9, 10, 11, 14, 22, 24, 26
Bin Ren, Yahui Liu, Yue Song, Wei Bi, Rita Cucchiara, Nicu Sebe, and Wei Wang. Masked jigsaw puzzle: A versatile position embedding for vision transformers. In CVPR, pp.
2023a. 3
Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan, Mengyuan Liu, Rita Cucchiara, Luc V Gool, Ming- Hsuan Yang, and Nicu Sebe. Sharing key semantics in transformer makes efficient image restoration. NewrIPS, 37: 7427 7463, 2024a. 1,
Bin Ron, Yawci Li, Nancy Mchta, Radu Timofte, Hongyuan Yu, Cheng Wan, Yuxin Hong, Bingnan Han, Zhuoyuan Wu, Yajun Zou, et al. The ninth ntire 2024 efficient susertresolttonn challenge report. In CVPR, pp. 6595-6631, 6631, 2024b.
19

---

Published in Transactions on Machine Learning Research (02/2026)
Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In CVPR, pp. 3937-3946, 2019. 3
Mengwei Ren, Mauricio Delbracio, Hossein Talebi, Guido Gerig, and Peyman Milanfar. Multiscale structure guided diffusion for image deblurring. In ICCV, pp- 10721-10733, 2023b. 3,
Wenqi Ren, Jinshan Pan, Hua Zhang, Xiaochun Cao, and Ming- Yang. Single image dehazing via multi-scale convolutional neural networks with holistic edges. IJCV, 128:240-255 259, 2020.
William Hadley Richardson. Bayesian- based iterative method of image restoration. Journal of the Optical Society of America, 62(1):55--9, 1972. 2
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In MICCAI, pp. 234-241, 2015. 5
Xiaole Tang, Xiang Gu, Xiaoyi He, Xin Hu, and Jian Sun. Degradation-aware residual-conditionee optimal transport for unified image restoration. TPAMI, 2025. 3,
Chunwei Tian, Yong Xu, and Wangmeng Zuo. Image denoising using deep cmn with batch renormalization. Neural Networks, 2020. 10
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. MAXIM: Multi-axis mlp for image processing. In CVPR, pp. 5769 3769-5780, 2022.
Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M Patel. WrathWeather: Transformer-based restoration of images degraded by adverse weather conditions. In CVPR, pp.
2022. 10
Cong Wang, Jinshan Pan, Wei Wang, Jiangxin Dong, Mengzhu Wang, Yakun Ju, and Junyang Chen. Promptrestorer: A prompting image restoration method with degradation perception. NewrIPS, 36: 8898-8912, 2023a. 3
Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy, Recovering realistic texture in image super-resolution by deep spatial feature transform. In CVPR, pp. 606-615, 2018. 3
Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffusion null-space model. ICLR, 2023b. 2
Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In CVPR, pp. 17683-17693, 2022.
Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808 04560, 2018. 22
Gang Wu, Junjun Jiang, Kui Jiang, and Xianming Liu. Harmony in diversity: Improving all-in-one image restoration via multi-task collaboration. In ACM MM, pp. 6015-6023, 2024a. 10
Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. Contrastive learning for compact single image dehazing. In CVPR, pp. 10551-10560, 2021.
Hongjee Wu, Linchao He, Mingqin Zhang, Dongdong Chen, Kunming Luo, Mengting Luo, Ji-Zhe Zhou, Hu Chen, and Jiancheng Lv. Diffusion posterior proximal sampling for image rostoration. In ACM MM, pp. 214 223, 2024b.
Zhijian Wu, Jun Li, Yang Hu, and Dingjiang Huang. Compacter: A lightweight transformer for image restoration. In ACM MM, pp. 3094 3103, 2024c. 1
Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. Learning texture transformer network for image super-resolution. In CVPR, pp. 5791-5800, 2020. 22
Mingde Yao, Ruikang Xu, Yuanshen Guan, Jie Huang, and Zhiwei Xiong. Neural degradation representation learning for all-in-one image restoration. JEFE TIP, 2024. 10
20

---

Published in Transactions on Machine Learning Research (02/2026)
Zhengwei Yin, Guixu Lin, Mengshun Hu, Hao Zhang, and Yingiang Zheng. Flexir: Towards flexible and manipulable image restoration. In ACM MM, pp. 6143-6111110 6152, 2024.
Zongsheng Yue, Jianyi Wang, and Chen Change Loy. ResShift: Efficient diffusion model for image super- resolution by residual shifting. arXtv preprint 0rXiv:230.123466 2023. 2
Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yulun Zhang, and Radu Timofte. See more details: Efficient. image super-rosolution by experts mining. In ICML. PMLR, 2024. 2
Eduard Zamfir, Zongwei Wu, Nancy Mehla, Yuedong Tan, Danda Pani Paudel, Yulun Zhang, and Radu Timofte. Complexity experts are task-discriminative learners for any image restoration. In CVPR, 2025. 3, 8, 9, 10, 11, 22
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pp. 14821-14831, 2021. 2, 10
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pp. 5739-5739, 2022. 2, 6, 10, 11
Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, and Feng Zhao. Ingredient-orientel mulli-degradation learning for image restoration. In CVPR, pp. 5825 5835, 2023. 2, 3, 9, 10, 11
Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE TIP, (1(7))314223155 2017a. 3
Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. Learning deep enn denoiser prior for image restoration. In CVPR, pp. 1929,,938, 2017b. 3
Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, and Shuhang Gu. Transcending the limit of local window: Advanced ruser-resolution transformer with adaptive token dictionary. arXiv preprint arXiv:240108820 088009 2024.
Xu Zhang, Jiaqi Ma, Guoli Wang, Qian Zhang, Huan Zhang, and Lefei Zhang. Perceive-ir: Learning to perceive degradation better for all-in-one image restoration. TIP, 2025. 4
Yulun Zhang, Kumpeng Li, Kai Li, Bineng Zhong, and Yun Fu. Residual non-local attention networks for image restoration. arXiv preprint 11t3:190310000 2019. 3
Mengyi Zhao, Mengyuan Liu, Bin Ren, Shuling Dai, and Nicu Sebe. Denoising diffusion probabilistic models for action-conditioned 3d motion generation. In ICASSP, pp. 4225-4229, 2024.
Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe, and Zhun Zhong, Textual knowledge matters: Cross-modality co-teaching for generalized visual class discovery. In ECCV, pp. 41 58. Springer, 2024a. 2
Haiyang Zheng, Ruilin Zhang, and Hongpeng Wang. Deep image clustering based on curriculum learning and density information. In ICMR, pp. 330-338, 2024b.
Lianghui Zhu Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09411 2024. 3
Yurui Zhu, Tianyu Wang, Xueyang Fu, Xuanyu Yang, Xin Guo, Jifeng Dai, Yu Qiao, and Xiaowei Hu. Learning weather-general and weather-specific features for image restoration under multiple adverse weather conditions. In CVPR, pp. 21747-211 21758, 2023. 9
21

---

Published in Transactions on Machine Learning Research (02/2026)
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 singlo-task setting, we combine the BSD400 Arbelacz et al. (2010) and WED Ma et al. (2016) datasets, and corrupt the images with Gaussian noise at. levels E (15, 25, 503. BSD400 contains 400 training images, while WED includes 4,744 images. We evaluate the demoising performance on BSD68 Martin et al. (2001) and Urban100 Huang et al. (2015). For single-task deraining, we use Rain100L Yang et al. (2020), which provides 200 clean/raing image pairs for training and 100 pairs for testing. For single-task dehazing, we adopt the SOTS dataset Li et al. (2018), consisting of 72, 135 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 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-vl datasct Wei et al. (2018), consisting of 485 training images and 15 testing images. Note that for the denoising task under the 5-degradation setting, we report results using Gaussian noise with a 25. The training takes 130 epochs.
Composited Degradation Datasets. Regarding the composite degradation setting, use the CDD11 dataset Guo et al. (2024b). CDD11 consists of 1,183 training images for: (t) 4 kinds of single-degradation types: haze (H), low-light (L), rain (R), and snow (S); (ūi) 5 kinds of double-degradation types: low-light + haze (1+h), low-light++rin (L+R), low-light + snow (L+S), haze + rain (H+R), and haze + snow (H+S). (Mi) kinds of Triple-degradation type: low-light + haze + rain (L+H+R), and low-light + haze + snow (L+H+S). We train our method for 150 epochs (significantly fewer than the 200 epochs used in MoCF-IR Zamfir et al. (2025)), and we keep all other scttings unchangod.
Zero-Shot Underwater Image Enhancement Dataset. For the zero-shot underwater image enhancement setting, we follow the evaluation protocol of DCPT JiaKui et al. (2025) by directly applying our model, trained under the 5-degradation setting, on the UIEB dataset Li et al. (2019) without any finetuning. UIEB 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
Our AnylR framework is designed to be end-to-end trainable, eliminating the need for multi-stage optimization of individual components. The architecture features a robust 4-level encoder-decoder structure, characterized by varying numbers of Degradation Adaptation Blocks (DAB) at each level, specifically [3, 5, 5, 7] from highest to lowest level. Following established practices Potlapalli et al. (2024); Zamfir et al. (2025), we conducted training over 130 epochs via batch size of 32 for the All-in-One and mixed settings. Optimization employed the L1 loss and Fourier with the Adam optimizer Kingma & Ba (2015) (initial learning rate of 2 x 10 81 = 0.9, B2- = 0.999) and cosine decay schedule. During training, we employed random crops of size 1282 and applied horizontal and vertical flips as augmentations. All experiments were performed using 2 NVIDIA Tesla A100 (40G) GPUs.
We also propose two scaled variants of our AnylR, namely Tiny (AnyIR -T) and Small (AnyIR -S). As detailed in Tab. 11, these variants differ in terms of the number of Degradation Adaption Blocks (DAB) across scales, the input embedding dimension, the FFN expansion factor, and the number of refinement blocks.
22

---

Published in Transactions on Machine Learning Research (02/2026)
Table 11: The details of the tiny and small versions of the proposed AnyIR.
AnylRt -Tiny
Anylit -Small
The Number of the DAB crosses scales
[3, 5, 5, 7] 28
[4. 6, 6, 8] 32
The Input Embedding Dimension
The FEN Expansion Factor
2
2
The Number of the Refiuement Blocks
Params. (1) FLOPPs (4)
5.74M 26G
8.51M
39 G
Figure 12: 3D PCA Visualization between the Half-Split and our proposed Skip-Split, and the visual illustration of Skip-Split.
B Discussion And Analysis
The visual illustration of Skip-Split and what it brings. Fig. 12 presents a 3D PCA visualization comparing Half-Split and our proposed Skip-Split. The results show that Skip-Split yields a more uniform and well-spread feature distribution, suggesting stronger and more discriminative representations. For a more intuitive understanding, a side-by-side visual comparison is also provided in the right part of Fig. 12.
What does GatedDA bring that differs from attention? As the visualization result shown in Fig. 13, the t-SNE visualizations of the feature maps reveal a key distinction between GatedDA and attention mechanisms. While attention effectively captures global relationships and context, its feature representations tend to exhibit. less structural separation in the embedding space. In contrast, GatedDA introduces a localized focus on degraddation-seecffi regions, resulting in a more distinct clustering of features in the t-SNE space. This separation highlights GatedDA's ability to emphasize degradation--peedd -specific information, complementing the broader scope of attention. Together, this synergy allows GatedDA to enhance image restoration by targeting specific degradation patterns while leveraging the global context provided by attention. As evidenced in the restored outputs, this combination lcads to more accurate recovery of both global and local details.
Attention and GatedDA Feature Maps. To investigate the effectiveness of the proposed Degradation Adaptation Block (DAB), we provide a detailed visualization of key components in Fig. 15. These include the attention mechanism, the GatedDA modulc, and its components- aa B, and 7-along with the fused feature map (combining attention and GatedDA), and the final restored image.
From the visualizations of the attention feature map in the second row of Fig. 15 and the GatedDA feature map in the third row, it is evident that GatedDA effectively models degradation factors such rain, noise, and have. This ratesst the capability of GatedDA to capture and emphasize degradation--eeltted information automatically.
Role of a, B, and y in Degradation Modeling. To further analyze how a, B, and y within GatedDA contribute to degradation modeling, we refer to the visualizations in the 4th to 6th rows of Fig. 15. These maps show that a, B, and y consistently focus on various aspocts of the degraded regions, cach specializing in different levels or types of degradation. This specialization allows the model to adapt to varying degrees and types of degradation, making it more versatile and effective in the context of Image Restoration (IR) under a 'One-for-Any' setting.
Fused Attention and GatedDA Features. The fused feature map, combining attention and GatedDA, offers further insights (visualized in Fig. 15). Compared to the original attention map, the fused map exhibits a richer representation of dogradation, which equips the model with more comprehensive information for accurate restoration. In conclusion, the proposed DAB enhances the attention mechanism by embedding richer degradation information, thus improving restoration quality. The GatedDA module within DAB
23

---

Published in Transactions on Machine Learning Research (02/2020)
Nose Tiper
INNE of Atserion
INE CrGNNDA
Resorel
Figure 13: 1SNE comparison between altention and GatedDA of the proposed method.
Original Training Sampels
New Training Samples
125,000
100,000
75,000
50,000
25,000
0
Noisy Samples
Rainy Samples
Hazy Samples
Total Samples
Figure 14: Training samples statistics.
introduces flexibility and diversity in handling various types and levels of degradation, contributing to the overall robustness and effectiveness of our method.
Affect of data distribution. As illustrated in Fig. 14, the original training samples exhibit significant variation across different IR tasks. Based on this observation, we propose new set of training samples that over-represent the denoising samples and reduce the dehazing samples by half. This adjustment aims to achieve a more balanced data distribution. The results, presented in Tab. 12, demonstrate that training AnylR with this revised set of samples, even when using only 90% of the original total training samples, yiclds significant performance improvements.
C Additional Results
Full Inference Time Comparison. Table 13 provides a detailed comparison of the full inference time between our method, AnyIR, and PromptIR Potlapalli et al. (2024) under the 3-degradation all-in-one IR setting. Across all tasks, method demonstrates significantly faster inference, highlighting its computational
24

---

Published in Transactions on Machine Learning Research (02/2026)
did (rigam
pog (eigma
Gased1A
g
Food
Oxappr
Figure 15: Visualization of the feature maps of each part within the proposed Degradation Adaptation Block (DAB).
25

---

Published in Transactions on Machine Learning Research (02/2026)
Table 12: Average results for denoising and overall (i.e. dehazing, deraining, and denoising) under the 3-degradation IR sctting.
Method
Denoising
Overall
PromptIR Potlapalli al. (2024)
31.11
.873
32.06
.913
Anyllt (Original, ours)
31.26
.878
32.80
.920
AnylR (New, ours)
31.31
32.89
.921
Table 13: Full inference time comparison under the 3-degradation IR sctting. The lower the better.
Task
Num. Samples
PromptIR Potlapalli et al. (2024)
AnyIR (Ours)
Denoising 15)
68
41s
30s
Denoising (ar 25)
68
41s
30s
Denoising (o 50)
68
42s
30s
Deraining
100
60s
43s
Dehazing
500
416s
274s
Framus Per Second
1.34
1.97
efficiency. For denoising lasks with varying noise levels (a = 15, 25, 50), AnylR reduces the inference time from 41-42 seconds to just 30 seconds, achieving over a 25% improvement. Similarly, for deraining, AnylR processes 100 samples in 43 seconds, compared to 60 seconds for PromptIR, and for dehazing, it processes 500 samples in 274 seconds, substantially faster than PromptIR's 416 seconds. In terms of frames per second (FPS), AnyIR achieves 1.97 FPS, outperforming PromptlR's 1.34 FPS by nearly 50%. These results emphasize the efficiency of AnylR in handling various degradation tasks, making it highly suitable for practical applications where computational speed is critical without compromising performance.
These results indicate that our AnylR model not only maintains high performance but also offers substantial efficiency improvements, making it more suitable for real-time applications on resource-constrainee devices. The reduced inference time ensures faster processing, enabling seamless deployment in scenarios requiring rapid deaksig such real-time sal-time video restoration or on-device image enhancement. Additionally, the improvement in frames per second (FPS) demonstrates its practicality for large-scale datasets and streaming applications. By achieving effective balance between accuracy and speed, AnylR provides a compelling solution for efficient, high-performance image restoration.
D Additional visual results.
The low-light enhanced visual comparison is provided in Fig. 16. The visual comparison results under the 3-degradation IR setting are shown in Fig. 17. It shows that the proposed AnylR can effectively restore the clean image from its degraded counterparts compared to other comparison methods.
E Limitations and Future Work
Although the proposed AnylR achieves strong performance and efficiency under the current all-in-one image restoration (TR) sotting, also presents several limitations that define the scope of applicability.
A first limitation lies in the imbalance of degradation distribution in the benchmark training data. Certain degradations (e.g., haze rain) appear more frequently with wider variation than others, which may bias the model toward better performance on dominant categories while providing smaller gains on under- represented ones. In future work, we plan to investigate more balanced or curriculum-style degradation scheduling, as well as data reweighting strategies, in order to improve robustnness across heterogeneous degradation regimes.
In addition, while GatedDA and the spatial frequency fusion mechanism provide consistent improvements across mixed and spatially non-uniform degradations, they are not universally optimal under all conditions.
26

---

Published in Transactions on Machine Learning Research (02/2026)
Low-light Input
MoCE-IR-S
AnyIR (Ours)
GroundTruth
LOL:495
L0L:665
LOL:669
LOL:111
Figure 16: Additional visual comparison for log-tight enhancement under the 5-degradation setting.
Since GatedDA selectively amplifies locally degraded regions, its benefit. is weaker when degradations are globally uniform and largely dominated by global corruption. Likewise, our fusion design assumess complementary cues across spatial and frequency domains; when the degradation is strongly biased toward a single domain (e.g., purely frequency-structured noise), the contribution of the other branch may be limited. Furthermore, the efficiency of our design arises from reducing attention channel capacity and compensat it with wight local modeling, which trades some global modeling flexibility for improved computational economy. Exploring adaptive routing between attention and GatedDA, as well as lask-dependent fusion weighting, is a promising direction to mitigate these trade-offs.
Finally, clarify that in this work, "All-in-One" refers to single model jointly trained across the degra- dation lypes covered in our benchmark setting, rather than unrestricted universal solution for arbitrary degradations beyond this scope.
F Broader Impact
The development of our unified image restoration model has significant potential, extending its impact beyond technical advancements. By reducing model complexity and computational requirements, our approach makes high-qualite image restoration accessible on eenource-constraingg platforms like mobile devices. This enables efficient restoration in fields such as telemedicine, remote sensing, and digital archiving. Additionally, minimizing the computational footprint reduces the environmental impact of large-scale data processing, aligning with sustainable computing practices. The public availability of our code will further foster innovation and collaboration within the scientific community, setting new standards for efficiency and expanding practical applications in image restoration.
27

---

Published in Transactions on Machine Learning Research (02/2026)
Degraded Inpur
PromptER
AnyIR (Ours)
Detaving (10-0)
Delacing (1406)
Dancssing (00044)) Danotsing (223001))
Daning (057)
Decing (0e-4)
Figure 17: Additional visual comparison under the 3-degradation setting.
28


Reviews:
[{"review_id": "5r8kwK3qwX", "reviewer": "Reviewer_BFhq", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Authors of this paper focused on the unified and efficient all-in-one image restoration model called any image restoration (AnyIR), where a single encoder and decoder architecture is used to model degradation-specific cues and invariance structures. It is realized by introducing a new degradation adaptation block (DAB) to decouple global and local processing as two pathways via skip-split strategy, and then a spatial-frequency fusion strategy is to unify two pathways to produce the fused representation. Extensive experiments on diverse restoration tasks show that the proposed model is effective and efficient compare to the baseline.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The proposed framework in Figure 3 is convincing with strong evidence. It consists of several important components including the skip split, the attention branch for global invariant information and the gatedDA for local degradation-sensitive information, a fusion on both spatial and frequency domain for further enhanced representations. Ablation study and parameter sensitivity analysis provided supports for the proposed model.\n\nAuthors showed evidence that skip split can be better than half-split in Figure 9 and it becomes effective during the fusion stage to avoid information loss.\n\nAuthors did present the impact of different settings for parameters $\\alpha, \\beta$ and $\\gamma$ in Table 8 and showed that all three parts are important to have. One suggestion on gated degradation adaptation in section 3.4 is to have a deep discussion on how local degradation-sensitive information for various restoration tasks could be modeled by the given parameterization of the split step in step 6 of Algorithm 1. For example, on three-degradation, what are these values for each degradation and how are they different. This may be helpful to better understand why the proposed model can work for various restoration tasks.", "audience_yn": "Yes", "audience_explanation": "Image restoration is an important task in image processing and computer vision. The focus of this paper is to attempt to learn a single and efficient model for handling diverse restoration tasks. The experimental results on various datasets in different settings show that the proposed model not only achieve start-of-the-art performance but also reduces model parameters comparing to the baseline. It could be particularly relevant for resource-constrained or edge deployment.", "requested_changes": "The section references in Figure 3 (b) are not consistent with the main context of the paper, that is GatedDA in section 3.4 and fusion in section 3.5. The figure should be revised.\n\nThe gated degradation adaption in section 3.4 is missing in details. Even though Algorithm 1 is presented, some details and definitions are missing such as how split in setp 6 is done, and what the learned parameters $W_{depth}$, $W_{gate}$ and $W_{proj}$ are. The similar issue happens for spatial-frequency fusion algorithm in section 3.5. Authors need to explicitly provide the definitions of math symbols used in Algorithms. \n\nIn the reported results such as Table 2, there are two methods AnyIR-T (ours) and AnyIR-S (ours). Are they some variants of the proposed AnyIR? It is better to specify them in details.\n\nIn Figure 7, four different DAB variants were investigated. It is unclear that why either half-split or skip-split is applied if there is only attention pathway is used.\n\nwhat is the learned parameter $\\lambda$ in Table 10?", "broader_impact_concerns": "There is no concern on the ethical implication of the work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Gd7XQowtJX", "reviewer": "Reviewer_aLqh", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents an image processing model called AnyIR, a universal approach for image restoration across various degradations. The authors focus on the \"sub-latent\" space of each input to balance generality and performance for real-world image domains. The proposed method claims to attain state-of-the-art performance while effectively reducing the number of parameters. (Please note: this reviewer is not knowledgeable for previous SOTA models in this specific area).\n\nBased on my reading, a core contribution of this work is its universalist approach, where a single encoder-decoder framework is designed to generalize across all degradations, aiming for both efficiency and robustness. The overall architecture closely resembles primarily U-shaped architectures, with the exception of the newly presented Degradation Adaptation Block (DAB). The DAB appears to be a residual-style transformer block employing GatedDA, a gated convolution mechanism based on 1×1 convolutions, designed to capture fine-grained, local degradation-aware details. Another notable mechanism is the fusion between attention and gated convolution in the spatial-frequency domain using real-valued FFT and iFFT operations.", "claims_and_evidence_yn": "Yes", "claims_explanation": "I believe one of the main claims of this paper is the performance evidenced by the set of benchmark scores. This work also transparently reports the number of parameters and memory usage, which will clearly aid future studies. The overall presentation is solid and clearly conveys the authors' main arguments.\n\n**However**, I must note that this paper in its current form does not clearly substantiate the claim regarding the \"All-in-One\" IR approach.\n* The authors mention that many existing solutions impose heavy computational burdens and limit deployment accessibility, motivating the pursuit of a single efficient model capable of addressing any degradation type. This raises a further question regarding the motivation for each architectural choice within the DAB (e.g., the skip-split strategy, the gated mechanism, and frequency-based fusion).\n* As an educated guess for a non-specialist in this domain, I suspect that the majority of the efficiency AnyIR provides stems from AnyIR's U-shaped architecture (and the internal residual structure in the DAB). While ablation studies identified reasonable winners regarding other intra-architectural changes, considering the complexity of the self-attention mechanism, the overall performance differences appear somewhat marginal. Therefore, I suspect that most baselines in Table 2 do not utilize a U-shaped architecture; consequently, the impact of other changes may be overestimated throughout the paper. The authors are encouraged to correct me if this is a misunderstanding, but I suggest providing specific justification and attributing performance gains to each component more clearly.\n\n\n* I find the authors' perspective on image restoration to be quite broad; Section 5 seems to fit into general image processing problems involving prediction. A reasonable question is whether AnyIR is specialized enough to be considered a dedicated IR algorithm, or if it is a general image processing model. The motivation presented in Sections 1 and 2 does not necessarily frame the framework as constrained only to image restoration (it feels too general). In this view, the paper requires revision to either refine its claim regarding image restoration and the sub-latent space or provide experiments beyond the IR problem.", "audience_yn": "Yes", "audience_explanation": "To the best of my knowledge, image restoration is a long-standing and significant topic in image processing. While some of the TMLR audience may prefer theoretical findings in novel areas, I believe the empirical results and architectural efficiency presented here would be of interest to practitioners in the field.", "requested_changes": "* Since Fig. 1 appears before the abstract, it must be exceptionally clear and accessible to a general audience. The authors are encouraged to enhance the presentation style of Fig. 1 by increasing the font size and clearly magnifying the denoising outputs.\n* Please include illustrative or qualitative supplementary information for Section 3 that better frames this paper as an efficient method.\n* Clearly discuss the motivation and scope of AnyIR and the subcomponents appearing in Sections 1–3.", "broader_impact_concerns": "N/A", "additional_comments": "* I believe Fig. 3 requires updates regarding references (e.g., GatedDA is in Section 3.4, Fusion is in Section 3.5).\n* Please discuss the strengths and weaknesses of the methods in Eqs. (2-5). I believe the All-in-One approach has noticeable downsides that AnyIR must address in this paper. I also suggest that Section 3.1 (Preliminaries) would benefit from being presented as a full-blown section with sufficient discussion, citations, and motivational remarks.", "extra_scores": {}, "extra_text": {}}, {"review_id": "nMQy6fuF8N", "reviewer": "Reviewer_1y7r", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposed a new unified framework based on diffusion models and proposed new modules to resolve image restoration via one single model. The results show that generation performance is competitive and efficiency is good.", "claims_and_evidence_yn": "No", "claims_explanation": "1. I feel the reasons for using one model are not motivated. One reason I can imagine is because of the system burden. For mobile environment, it will become more challenging for multi-step methods as you may encounter issues deploying modules across different systems. Authors can elaborate more on the motivation, for example, some evidence in the real-world.\n2. The idea that we should use a unified solution for different kinds of degradation is elegant. Authors have proved that such an idea is resolvable with evidence.\n3. A core question I am thinking of is that to make $x=D(E(y))$ work, we need to make sure that $D_z$ is invertible, which means we can convert $y$ back to $x$. If $D_z(x_1)+\\epsilon=D_z(x_2)+\\epsilon=y$, how do you determine the result of $D(E(y))$. Is it $x_1$ or $x_2$?\n4. Do you use FCN, CNN, or MOE for FNN?\n5. In GatedDA, why GeLU is separated with a parameter $\\gamma$? What is the reason that put it before feeding into the next layer (module), which is more common in previous architectures. The reason why I am asking this is 1. GeLU is mostly used in transformer structures such as BERT and GPT. 2. The reason for GeLU is that you make near zero activation still continuous, removing discontinuity and making it smoother around zero point compared to ReLU. So, in this way, you can effectively avoid dead neurons. But basically in Figure 4, GateDA already has a skip connection which can propagate residual and avoid information sudden lose. A more reasonable way is to put GeLU at the end. But I would be happy to see the authors explanation on that and correct my judgment.\n6. The architecture is similar to diffusion models. But you use patch encoding, which is used in ViT rather than a latent encoder, what is the reason for that? Do you think it will work if you use a latent encoder?\n7. Do you have the comparison to direct usage of a diffusion model In image editing, with a stable diffusion and DDPM, you can easily recover an image and remove the perturbation on an image.", "audience_yn": "Yes", "audience_explanation": "The studied question is important in certain fields and it is important to develop a unified, simple, and efficient image restoration solution over mobile devices. Such a topic is interesting and important to certain advancements.", "requested_changes": "I raised several questions and confusion in the second section. Certain changes are needed to eliminate the concerns.\n\nSome minor changes but may help improve the paper. The writing needs pretty much improvement for a round of revision.\n1. I would suggest not putting Figure 1. at the beginning because without reading the paper, it is hard to get the general idea of what Figure 1 is doing. Put it at the proper place it is referred is fine, usually in the main text. Also, it is not a good idea to put all results in one figure. (a) (b) are quantitive results. (d) are visualization results. (c) is efficiency results. Those three should be separated in three figures and captions should contain necessary description.\n2. At the beginning of the introduction, giving an equation directly will disrupt the reading experience as you need to spend paragraphs explaining each notation. The introduction should be straightforward and directly cut to the chase about what you are going to do.\n3. Authors should directly elaborate what questions you want to resolve instead of putting a lot of literature at the beginning of the introduction. Better manner is deferring the discussion of them to related work.\n4. Figure 3 is confusing also. After reading the paper, I think it means you take an image in and and output out and you can do this in batch style. But figure 3 makes me feel that the paper has some specific optimization for batch input in the discussed task.", "broader_impact_concerns": "Ethical implications are sufficiently addressed.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)