UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity
Authors: Jingbo Lin
Zhilu Zhang
Wenbo Li
Renjing Pei
Hang Xu
Hongzhi Zhang
Wangmeng Zuo
Venue: ICLR 2026 Poster (2026)
Keywords: low-level vision
image restoration
all-in-one image restoration
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in leveraging degradation estimation-based priors, and the latter suffer from the inevitable error in degradation estimation. Consequently, the performance of existing methods has a large gap compared to specific single-task models. In this work, we make a step forward in this topic, and present our UniRestorer with improved restoration performance. Specifically, we perform hierarchical clustering on degradation space, and train a multi-granularity mixture-of-experts (MoE) restoration model. Then, UniRestorer adopts both degradation and granularity estimation to adaptively select an appropriate expert for image restoration. In contrast to existing degradation-agnostic and -aware methods, UniRestorer can leverage degradation estimation to benefit degradation-specific restoration, and use granularity estimation to make the model robust to degradation estimation error. Experimental results show that our UniRestorer outperforms state-of-the-art all-in-one methods by a large margin, and is promising in closing the performance gap to specific single-task models. The code and pre-trained models will be publicly available.

Published as a conference paper at ICLR 2026
UNIRESTORER: UNIVERSAL IMAGE RESTORATION VIA ADAPTIVELY ESTIMATING IMAGE DEGRADATION AT PROPER GRANULARITY
Jingbo Lin', Zhilu Zhang', Wenbo Lie, Hang Xu², Hongzhi Zhang', Wangmeng Zuo Harbin Institute of Technology, Huawei Noah's Ark Lab jibliinss 9 gmaim 1. com)
ABSTRACT
Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradatiin-aggnoste or degradation- aware. However, the former are limited in leveraging degradation estimation- basdd priors, and the latter suffer from the inevitable error degradation estimation. Conse- quently, the performance of existing methods has large gap compared to specific single-task models. In this work, we make a step forward in this topic, and present our UniRestorer with improved restoration performance. Specifically, we per- form hierarchical clustering on degradation space and train a -ratuagran cexture-of-exppere (MoE) restoration model. Then, UniRestorer adopts both degra- dation and granularity estimation to adaptively select an appropriate expert for image restoration. In contrast to existing degradation-agnostii and -aware methods, UniRestorer can leverage degradation estimation to benefit degradation-speed restoration and use granularity estimation to make the model robust to degradation estimation error. Experimental results show that our UniRestorer outperforms state-of-the-art all-in-one methods by a large margin, and is promising in closing the performance gap to specific single-task models. The code and pre-trained models are released at https://iitubb.om/mmuuui Uuiteersrriinnntteeerrr...r
INTRODUCTION
Image restoration is a fundamental task computer vision, it aims to restore high-quality (HQ)
images from corresponding low-quality (LQ) counterparts, including denoising (Zhang et al., 2021b;
Abdelhamed et al., 2018), deblurring (Zhang et al., 2022c; Nah et al., 2017a; Rim et al., 2020), de- le-weethering (Liu et al., 2024; 2018a; Fu et al., 2017), low-light enhancement (Zhang et al., 2024b; Wei et al., 2011; Xu et al., 2022), etc. In the past decade, advanced deep neural architectures (He al., 2016; Vaswani et al., 2017: Liu et al., 2021; Zhang et al., 2023b; Radford al., 2021; Li et al., 2024;?; Kong et al., 2025) have driven image restoration methods to evolve from task-specific (Zhang al., 2017; Dong al., 2014; Kupyn et al., 2019; Ren et al., 2019; Song al., 2023; Liu et al.,
2018b; Zhang et al., 2021c; 2022b; 2024a; 2025) to aank-agi (Zamir et al., 2021; Liang et al., 2021: Chen al., 2022: Zamir et al.. 2022; Lin et al., 2024) backbones. Recently, influenced by the success of foundation models in natural language processing and high-level vision, there has been increasing interest in addressing multiple image restoration tasks within a single framework, known as all-in-one image restoration (Jiang et al., 2024).
straightforward approach is to utilize data from all tasks to train a degradation-agnossii model (see Fig. 1 (a)), but it easily yields unsatisfactory results due to the conflicts between certain tasks (e.g., denoising and deblurring). Inspired by gurameter-efficiee fine- see-tunnng (PEFT) methods (Brian et al., 2021; Houlsby et al., 2019), learnable prompts (Li et al., 2022a; Zhang et al., 2023a; Potlapalli ct al., 2023; Valanarasu et al., 2022; Guo et al., 2024; Xiangtao et al., 2024) and adapters (Ai et al., 2024b) are integrated into the restoration model to modulate intermediate features (see Fig. (b)), allowing the model to process different tasks more specifically. Furthermore, some methods introduce mixture-of-experts (MoE) (Yang al., 2023; Chen al., 2024; Cao et al., 2024b; Wenlong et al.,
"Corresponding author.

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Promyet
Hlerarchical chastering of degrafation representations
Coarser
Enooder
Encoder
All dkgee
ino
granularity elgo
Decoder
Becoder
Addytione
Inputs
(0) Degradition
Degradation (Veg)
aprostic tramework
(b) Pltri-iareed framework
Degradation & granubrity bised router
Truin
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Routing deg.
Routing gran.
о D0-00
Fnonder
Milli-gramuhentt Mob model
Decoder
Decoder
LaRA
based trunework
framewock
Figure 1: Illustration of representative all-in-one image restoration frameworks. (a) Degradation-agnostic methods (Wang et al., 2021; Zhang et al., 2021a) train a shared backbone using data from all tasks and are limited in leveraging degradation priors. (b) PEFT-based methods apply learnable prompts adapters to the backbone to adapt various tasks. (c) Expert-basee based methods train specific MoE, LoRA, or filters for specitic tasks. However, both (b) and (c) suffer from the inevitable error in degradation estimation. (d) We construct a muulti-grauulartyy degradation set and a multi-granularigg MoF. restoration model. By jointly leveraging the degradation- and gramularity-ban hannlarrty-based router, our UniRestorer can be effective in leveraging iegradation-specifne restoration while being robust to degradation estimation error.
2023; Li et al., 2022c) (MoE-like using filters (Park et al., 2023; Cao et al., 2024a; Conde et al., 2024; Liang et al., 2022; Xu et al., 2024; Zhu et al., 2023) and low-rank adaptation (LoRA) (Tian et al., 2024; Ai et al., 2024a; Zamfir et al., 2025)) modules, routing the current feature to specific experts based on deep features and estimated degradation (see Fig. (c)).
Despite rapid progress, these methods show limited performance and fall significantly behind specific single-task models. eegraddttionaggnosie models cannot leverage the benefits from degradation estimation-baser priors, limiting their effectiveness in all-in-one image restoration. PEFT-based and MoB-based methods provide degradation-aware ways for processing different corruptions by conditioning on degradation estimation. However, their effectiveness heavily relies on accurate degradation estimation, which inherently challenging due to the vast and diverse degradation space on-ein-ee image restoration. Consequently, degradation- aware models are inevitably vulnerable to
of all-in one estimation errors, which can lead inappropriate prompt or expert routing, ultimately degrading restoration quality.
From the above analysis, one plausible solution to improve all-in-one restoration is to estimate and leverage image degradation while being robust to degradation estimation error. To this end, we propose to represent image degradation multiple levels of granularities rather than single-level, and construct multi-granularity degradation representation (DR) set by hierarchizing the degradation space. We then build a corresponding multi-granularrty MoE restoration model, where fine-grained experts specialize in specific degradations to enhance restoration accuracy, while coarse- grrssee-raindd experts generalize across broader degradation spaces. To enable more robust expert routing for degraded image, beyond vanilla degradation estimation, indicate the degree of degradation estimation error. For example, in Fig. (d), given an image suffering from large snow degradation, if the degradation estimation ambiguous, the granularity estimation will assign coarser-grained expert, e.g., an expert for general snow, to alleviate the effect of estimation error. Conversely, when the degradation estimation is confident, granularity estimation will assign a finer-grained expert to maximize the restoration quality, e.g., an expert for large snow. By jointly leveraging both degradation and granularity estimation, our approach can mitigate the adverse effects of inaccurate degradation estimation, achieving high restoration and generalization performance simultaneously.
introduce granularity estimation to
Specifically, we take the following steps to implement our UniRestorer. First, we optimize degradation extractor that is aware of finer-grained image degradation. Second, all low-quality images in the training set are fed into the degradation extractor, and their features are then hierarchically clustered into multiple DR groups at different granularities, respectively, which forms the multi- granularity degradation set. Third, train multi craliiirraulartty MoE restoration model based on the constructed multi- tittiirranllattty degradation set. Fourth, given images with unknown degradations, we learn routers based both degradation and granularity estimation results. The router identifies which
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Figure 2: Comparisons with task-agnostic methods, all-in-one methods, and single-task models. Ours and Oursèdenote auto and instruction modes, which are respectively used for a fair comparison with all-in-one and specific single-task models. (a) Comparisons on single-degradation all-in-one setting. (b) Comparisons with specific single-task models. (e) Comparisons mixed-degradation all-in-onc (in-of-distribution setting. (d) Comparisons mixed-degradation all-in-one (out-of-distribution) setting.
DR group the degradation and granularity best match in the degradation set, and then allocates the corresponding expert for inference.
To demonstrate the effectiveness of the proposed method, we conduct experiments on both single- degradation and mixee-degradatton scenarios. For the single-degradation scenario, we unify seven widely studied image restoration tasks. For the mixed-degradation scenario, we evaluate on the CDD-11 (Guo al., 2024) benchmark dataset. In addition, we conduct a comprehensive comparative evaluation. The results indicate that our method not only significantly outperforms existing all-in- one methods but also achieves performance that is competitive with, or even superior to, specific single-task models (see Fig. 2).
The main contributions can be summarized as follows:
We propose UniRestorer, a novel all-in-one image restoration framework that can estimate and leverage degradation representation to adaptively handle diverse image corruptions.
We propose a
MoE restoration model, where finer-experts specialize in specific degradations to enhance restoration accuracy, while coarser-experts generalize on broader degradation spaces to ensure generalization ability.
To alleviate the impact degradation estimation error on expert routing, we propose to estimate proper granularity for image degradation. By jointly leveraging degradation and granularity estimation, our method can adaptively allocate the most appropriate expert.
Extensive experiments are conducted on singlc-degradation and mixed- deddrederaniinnn scenar- ios, showing that our method significantly outperforms existing all-in-one methods and effectively closes the performance gap with specific single-task models.
RELATED WORK
Image Restoration for Specific Tasks Pioneer works propose tailored frameworks for specific
degradations, e.g., DnCNN (Zhang et al., 2017) for denoising, SRCNN (Dong al., 2014) for superresolution, DeblurGANv2 (Kupyn et al., 2019) for deblurring. PReNet (Ren et al., 2019) for draining, Dehazeformer (Song et al., 2023) for dehazing, and DesnowNet (Liu et al., 2018b) for desnowing. Benefits from advanced deep neural networks, some works tend to develop task-agnostic models, such as SwinIR (Liang et al., 2021), NAFNet (Chen al., 2022), and Restormer (Zamir et al., 2022), which become strong baselines in various image restoration tasks.
PEsT-bbased All-in-One Image Restoration To adapt to various corruptions, pioneer all-in-one image restoration methods (Potlapalli et al., 2023; Zhang et al., 2023a; Valanarasu et al., 2022; Li al., 2022a; Xiangtao et al., 2024; Ai et al., 2024b) integrate PEFT-based techniques (Brian et al., 2021; Houlsby et al., 2019) and feature modulation (Zhang et al., 2018) into the restoration backbone. For example, Trans Weather (Valanarasu et al., 2022) and AirNet (Li et al., 2022a) learn degradation-related embeddings as prompts to modulate deep features based on different corrupted inputs. Similarly, PromptIR (Potlapalli et al., 2023) and IDR (Zhang et al., 2023a) employ prompt learning in powerful backbone (Zamir et al., 2022) and achieve state-of-the -ft performance. MiOIR (Xiangtao et al., 2024) further exploits the benefits of incorporating explicit prompt and adaptive prompt learning. OneRestore (Guo et al., 2024) learns text-aligned prompts and achieves
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good results in restoring mixed diixedddegradation. However, prompt learning- based techniques are still limited in performance and task range.
Expert-based All-in-One Image Restoration caaturre-f-Expertti (MoE) has proven to be solution to multi-task learning due to its conditional processing capability (Chen et al., 2023b;a; Zhu et al., 2022). Recent MoE-based all-in-one image restoration methods can be categorized into implicit MoE (Yang et al., 2023; Conde et al., 2024; Xu et al., 2024; Cao et al., 2024a) and explicit MoE (Zhu et al., 2023; Park et al., 2023; Zamfir et al., 2024; Cao et al., 2024b; Tian al., 2024; Chen et al., 2024; Ai et al., 2024a; Zamfir et al., 2025). The experts in the former methods are implicitly learned and do not correspond to specific task. The explicit MoE methods learn experts for specific tasks, unleashing the power of additionally increased parameters. Based on a shared backbone, WGWSNet (Zhu et al., 2023) and ADMS (Park al., 2023) learn specific filters for specific tasks. To be more efficient, DaAIR (Zamfir et al., 2024), InstructIPT (Tian et al., 2024), and LoRA-IR (Ai et al., 2024a) learn individual LoRA weights or adapters for each task. MoCE-IR (Zamfir al., 2025) learns complexity-awee backbone and task-specific modules, these methods can adapt to different tasks effectively. In addition, GRIDS (Cao al., 2024b) and RestoreAgent (Chen et al., 2024) divide the complete degradation space into subspaces and learn a non non-shared full- parameeerr model for each subspace to alleviate potential conflicts between different degradations.
experts for different tasks. By combining knowledge of the shared
However, these methods mostly learn DRs at single, coarse granularity and rely solely on degradation estimation the routing condition, making them vulnerable to estimation errors. In contrast, our approach explores DRs across multiple granularities, jointly utilizing coarse levels (the same as task-agnostic settings) and finer levels beyond those used in existing methods. The integration of granularity and degradation estimation in the routing process enables more robust expert selection.
3 PROPOSED METHOD
In this section, we introduce our all all-in-one image restoration framework, UniRestorer. We first optimize one degradation representation (DR) extractor that learns DRs at fine-grained level. Based on DRs extracted from all low-quality training data, we conduct hierarchical data clustering that separates DRs into multiple DR groups at different granularities (see Fig. 3), leading to gualti-gramularity degradation set and MoE restoration model. To alleviate the effect of degradation estimation
routing, we adopt both degradation and granularity estimation to allocate the most appropriate expert to given unknown corrupted inputs.
3.1 PRELIMINARIES
ceature-of-Experree (MoE) MoE is promising way for scaling up and deploying large or gigantic models due to its competitive computational bounds and latency compared to the vanilla single model. Generally, the MoE architecture consists of two key components: set of experts and routing network G. Specifically, the expert set F ===(6 {F1,- J. contains n parallel modules (e.g., convolution layers or feed-forward layers). Given input æ, the routing G is used calculate the contribution (xx of each expert F;(x)) to the MoE output y.
y = Σ (()) ==0
(I)
where h is one representation of æ. G is typically a noisy top-k network parameterized with W. and W.. which models (z) as the probability of using the i-th expert J, and selects experts with top-k probability to contribute to the output. The noisy routing process can be descrihed as follows,
G(h)= = Top- K (Softmax W, + e Softplush....)
e <~N(00 1).
(2)
where Top-K(-) sets all elements in the vector to zero except the elements with the largest & values, Softplus is the smooth approximation the function ReLU.
3.2 MULTT-GRANULARITY DEGRADATION AND EXPERT
Fine-Grained DR Extractor Considering the convenience of using text to represent the type and degree of degradation, we adopt CLIP-based method, DA-CLIP (Luo et al., 2024), as basic
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Mults-Grnuulartt Degradation Set Construction
0:dh level
GOMEEESE
Deg. extractor
Hiremnhied
1-th leve
clustering
inputs
Routing via Degradation and Granularity Estimation
multi collll-grlllritty MoE model
Instruction (optional)
Pruning
111 00 000
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Grmmdurity- based router
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94
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LQ inputs:
HQ predictions
Illustration of our proposed UniRestorer. We develop multi-granularity degradation set by hierarchical clustering on extracted DRs at different granularities. Based on multi- gramularaallrrtty degradation set, we train a multi-granularity MoF. restoration model. Besides vamilla degradation estimation, we introduce granularity estimation to indicate the degree of degradation estimation error. Based on both degradation and granularity estimation, the router adaptively allocate appropriate expert for unknown corrupted input.
Figure 3:
backbone of the DR extractor. Using contrastive learning, DA-CLIP trains image controller to distinguish different corruptions. In this work, we aim to enable the DR extractor to be aware of finer-grained DRs. To this end, construct fine- tinengraneed degradation captions for low-quality images, refining the degradation type into the degradation degree captions. Take Gaussian noise as an example, we divide it with level in range of [0, 50] into [0, 15), [15, 35), and [35, 50]. described as 'small noise', 'medium noise', and 'large noise', respectively. We then use BLIP (Li et al., 2022b) to generate captions for clean images. By combining low-quality images, clean images, as well their content and degradation captions, we can train fine- -eeigra DR extractor. Given low-quality input images X, the fine-grained DR e E RR can be obtained by the DR extractor D, i.e.,
e D(X).
When training finished, we employ to obtain the DR set { from training samples.
Hierarchical Degradation Clustering We adopt an unsupervised data clustering approach (Mitchell, 1997) construct the multi-granularity degradation set. The training samples are hierarchically clus- tered from top to down, thus constructing a hierarchical degradation set with levels of granularities 3). The coarsest level 0-th level and the finest level is (1- 1)-th level. Each level of granularity consists of non-overlapped DR groups, and samples drawn from a DR group have similar DRs, as shown in Fig. 3. Specifically, 1-th degradation set can be obtained from the carsest-grained (0-th) level set. First, we cluster the 0-th level set, i.e.,
{u K-means((e,
where { 1m is cluster centroids. Then, we use function argmin ue( (u. J. ( - -= to decide which cluster the sample e, belong to, and assign the sample to corresponding cluster. All samples in the same cluster form DR group. By analogy, conduct the same process based on newly introduced DR groups until the finest-grained level.
Multi-Granularics MoE Restoration Model based on the multi- muulti-ranuulaityy degradation set, where each expert corresponds to a specific DR group. Expert networks are trained with & loss as the reconstruction loss hetween predicted results and target Y, i.e..
We train multi caltti-raaularitty MoE restoration model
4 = Y Y
(5)
Experts trained at coarser coarser-grained levels can generalize a wider range of degradations, and experts trained in finer-grained levels highly improve the restoration performance degradations that
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they good at. Therefore, given corrupted inputs, estimate their DRs and assign them the most appropriate expert. implementation, on the one hand, we employ a full-parameter network for each expert and train it from scratch, which follow prior works such GRIDS (Cao et al., 2024b) and RestoreAgent (Chen et al., 2024). On the other hand, we train a LoRA (Hu et al., 2022) version derived from the coarsest-grained expert as the restoration model of other experts, which aims to reduce the redundancy of parameters. More details can be found in Sec. C of Appendix.
3.3 ROUTING VIA DEGRADATION AND GRANULARITY ESTIMATION
It makes sense to use estimated DRs as condition to assign experts to corrupted inputs. However, degradation estimation error- proor, especially the fine-grained level, which may lead inaccurate routing and thus impede restoration performance. To this end, based on the constructed multi- granularity degradation set, we estimate image degradation the proper granularity level, and perform routing based both degradation and granularity estimation, which makes our method robust to degradation estimation error.
Specifically, train two separate branches (i.e., Ha and H.y) upon D for generating degradation estimation vector ea E Rº at the umest-grained level and granularity estimation vector Eg E I.P Then, we train two routers (i.e., Ga and 92) as Eq. (2) based on degradation and granularity estimation, respectively. To clarify the routing process, we denote Fi, be the expert trained on j-th set of the i-th level, ((k) be the path from the k-th leaf to the root. Thus, p(t) can cover experts {Fe 1 from different granularities, where j the index of the selected expert on i-th level path p(k), Ga. the degradation estimation-bart -bsed routing, is conducted at the finest-grainet level. It is used to find which fine-grained group the current DR possibly belongs to. Based on Ga, we can obtain a set of experts in ((k) (as the orange solid line in Fig. 3), which can described as,
(Fozg: F1st
Fi-1st } argmax(Ga(e.))II1
Further. based on Gg. we can obtain set of experts at t-th level (as the gray solid line in Fig. 3), which can described as,
Fr......... Ftmm! argmax (rgmax(G((e.)IF))
where m, + 1 is the number of all experts in 1-th level. The routing result is the intersection of the above two routing processing,
F [Fo Fist.................... Fist (Fro.
Fr.m,
Inspired by data uncertainty learning (Chang et al., 2020), we learn ea and eg by introducing loss as,
Lay(Es: Eg) = E 2 e
1
eall log
where u E IRP is the cluster centroids in the finest-grained level. The loss builds the relationships of ea and eg. When the distance between current degradation estimation and corresponding weight center is high, larger eg is used to ensure that Lag is not too high. Therefore, the granularity can reflect the error of current degradation estimation. For stable training. we clip the value of e, to minimum value of 0.01. Further, we adopt load balancing loss (Shazeer et al., 2017) Lour as auxiliary loss. Finally, we train our routers by
Liotal l + a Lag + Launs
(10)
where and are two hyper- hyper-parameters to trade-off the effect of Lag and Laux- The detailed theoretical analysis of Lag seen in Sec. A of Appendix.
Instruction Mode Our framework can automatically conduct the processing pipeline of extraction- (i.e., auto mode). When users are convinced about the type of degradation, they can also provide the type of degradation to instruct the routing process (i.e., instruction mode), as shown in Fig. 3. The instruction plays the role of pruning, it utilizes set of masks to prune the corresponding DR groups from the multi- culli-granullrity degradation set, leading to a more efficient and accurate routing.
recognition-routing-rrstoooooooe

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Table 1: All-in-one image restoration (singledeegradation) results. PSNR (dB, 1) and SSIM (1) are reported in RGB color spaces. The best performance is highlighted. "n'T" means the average performance of tasks (3T: Derain, Dehaze, Denoise: 5T: Derain, Dehaze, Denoise, Deblur, Lowlight; 7T: all).
Methods
Derain Rain
Dehaze SOTS
Denolse BSD68
Dehlur GoPro
Lowlight LOLvi
Desnow Snow 100K
CAR LIVE
Average ST
7T
(Zamir 2021)
16.37
.124
SwinlR (Liang et al, 202.1)
21.20
.837
15.94
.775
24.12
780
16.86
.755
13.80
.409
18.60
.718
15.61
.746
17.55
17.09
18.45
NAFNer (Chen al. 2022)
$1.32
967
22.43
907
30.27
918
26.53
877
23.09
.756
26.33
889
31.26
946
24.15
25.76
26.28
Restormer (Zamiz st. 2022)
34.25
29.64
991
31.00
929
27.50
22.80
.811
28.24
31.21
30.46
28.56
28.27
2019)
AirNet-3T (1. nl, 2022g)
34.90
967
27.94
9662
31.06
1872
29.29
PrompriR-3T 3- (Potlapalli et 2025)
2023)
$6.37
972
50.58
974
31.12
.873
31.50
InstractRR33 (Conde 2024)
$7.98
.978
30.22
31.32
.875
31,49
D (Zamfir al., 2024)
PrompdR-TUR-3T (Wo et 2025)
38.57
.984
31.17
9978
31.19
.772
32.67
McCEIR-3T 31 (Zamfir et ol, 2025)
38.57
.984
31.34
.979
31.24
.873
32.73
DL-ST (Fan 2019)
21.96
762
20.54
822
23.09
745
19
19.83
.112
21.02
20.25
Tranrwather (Valaarasuu
TAPE-TT et ol, 2022a)
29.67
9034
22.
8611
30.18
8.55
24.47
765
18.97
.621
24.10 24.28
AirNet-5T (Li et 20222)
32.98
.951
21.04
884
30,91
882
24.35
781
18.18
7335
23.82 24,10
IDRSTT (2ang et 2023a)
35.65
.965
25.20
31.09
883
26.65
20.70
.820
27.36 26.86
InstracUR-5T (Condo
202()
DrAIR-ST (Zanffir
2024)
36.28
.75
31.97
980
31.07
878
29.51
890
22.38
.225
32.20
30.24
Transweather TUR-ST (Wa at. 2025)
TUR-
33.09
.952
68
966
30.40
369
26.63
815
23.02
8.38
29.96
28.56
DCPT-PrompdR-- ST (JaKui et al. 2025)
57.32
.978
30.72
977
31.32
885
28. 84
23.35
8:10
31.46
30.31
McCEIR-5T (Zanlir et
UniRestocer (Ours)
41.68
.996
36.44
984
31.43
936
31.48
948
26.67
8663
31.12
9400
30.57
.926
36.1
33.38 32.77
UniRestorer LeRA (Ours)
40.22
.911
34.94
.911
31.39
.937
30.86
.933
24.42
.827
30.53
.925
30.31
.922
35.51
32.36
31.81
Devining
NATNNa
Restomeer
AirNe
Prompt
Fasenee
Ours
Delizzing
NAFNot
Restomer
Prompui
Deneising
TQ
NACNot
Restomer
AirNe
Prempilk
InstRai
Our
Figure 4: Visual comparison of results all-in-one image restoration (singe-degradattion) Our method can effectively remove the degradations (i.e., rainstreak, haze, and noise), restore clearer texture (i.e., 'branches' and 'eyes' pointed by red arrows) and closer color (i.e., 'bridge"). More results can be seen in Appendix.
EXPERIMENTS
4.1 EXPERIMENTAL. SETUP
We train and evaluate our framework on single-degradation and mixed-degradatiin scenarios. The usage of datasets can checked in Tab. of Appendix. In comparison, we only evaluate on tasks of the methods support. More details of datasets, degradation pipeline, experimental setups, and training details can seen Sees. Band C of Appendix.
4.2 COMPARISON WITH STATE-OF-TIIE-AR METHONS
Comparison with All-in-One Models As shown in Tab. I and Fig. 4, our method can significantly outperform agnostic and all-in-one methods by a large margin single-degradation. In mixed- degradation (Tab. 2), method continuously achieves higher performance than other methods from
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Table 2: All-in-one image restoration -egraddttgra results on CDD-11 dataset. PSNR (dB, t) and
SSIM (1) are reported in RGB color spaces. The best performance highlighted.
Metloos
L-H
H-R
R-S
1-8.S
Avorge
AlrNe (I s000gg
34.81
776
34:31
311
161
919
21
21
TNN
53:87
700
11.3
271
53.31
566
401
716
20.33
1325
33:35
IRmpiRR (hutopall unal 2011
26-11
925
10
969
16
916
11 95
360
AM
05
771
34.11
760
24.54
974
70
435
28.74
155
23.35
747
25:90
34.19
774
364
975
100)
34
761
21:31
495
23.90
372
23.97
771
26-45
Wenthe (Oadesisci Legosaciin, 2023)
23.55
-63
358
21.25
22:49
(Guo 2024)
52.52
34.3
799
24.00
70]
NoCEIIK (Zandir ot 2023)
27.25
A24
33.66
950
34.31
370
35.91
350
24
817
26.25
500
26.04
793
29.33
964
30.19
370
25.41
750
25.30
750
29:05
Urikeeskrm (Ouzs)
28.25
J95
.98
27-54
23.87
399
33.34
J45
33,85
36:99
(Ouss)
Table 3: Generalization performance on real-world datasets. All methods are evaluated without additional training. PSNR (dB, 1) is reported in cchachhnnels.
Table 4: Generalization performance on unseen cor- ruptions. All methods are evaluated without training on corresponding datasets. PSNR (dB, 1) is reported in channelnssls.
Methods
Deraining Low-light Desuowing
Average
MPRNNr (Zamir et al. 2021)
29.16
9.67
25.82
27.10
Methods
Raindrop
UDC
Underwater
NAFNet (Chen at 2022)
SwielR (Linng ct 2011)
2021)
21.74
26.04
26-46
18.67
27.51
18.25
26.33
20.91
NAPNet (Chen ot 2022)
22.89
26.89
15,54
Resiommer (Zamir et al, 20221
27:25
28.92
26.73
27.26
Restormer (Zamir al,, 2022)
23.34
PinnyeRR (Potipalli al. 2012)
25.68
PromptR (Pollapalli et al., 2023)
22.98
25.02
16.12
MIIIR (X (Xisngtan et al., 20244
25.69
10:62
MiOIR (Xiangtao al,, 2024)
23.59
20.11
InsmmtlR (Conde 20044
28.63
50.31
OneRestore (Guo et al., 2024)
22.02
15.16
15.87
OncResture (Cso
20244
24.55
17.76
20.12
23.24
Unieestorer (Ouss
30,04
30.40
28.04
29.70
Unikestorer (Ours)
24.91
29.64
18.07
Un/ostoom--4 oRA (Chrs)
27.73
28.65
UniRestore-LoRA (Ours)
24.43
28.92
17.55
Table 5: Compare to single-task image restoration models. PSNR (dB, 1) are reported in RGB color spaces. The best performance is highlighted. The task-agnostic methods are trained from scratch on each task. + denotes instruction mode.
Methods
Derain R0in
Denoise BSD68
Desnow Snow 100K
Lowlight LOLvI
Debtur Dehaze GoPro
LIVEI
SOTS
m=13 15
m=25
m=50
I.
g=10
q=20
g-30
g=40
MPRNet (Zamir et al., 2021)
35.21
34,91
32.67
29.62
36.22
34.49
30.63
27
30.23
31.57
32.50
23.51
32.66
31.07
SwinlR (Liong al., 2021)
37.70
34.81
32.51
29.29
33445
32.00
28
26.16
28.62
29.93
30.81
19.87
29.09
28.35
Restormer (Zamiz et al., 2022)
39.60
34.92
32.70
29.70
36.50
34.77
30.86
27.89
30.21
31.56
32.48
23.66
32,92
33.18
NAFNet al,, 2022)
38.36
34.80
32,58
29.50
35.94
33.95
29.50
27.95
30,24
31,56
32.47
23.75
33,69
22.70
UniRestoneri(urr)
41.68
35.00
32.75
29.75
36,94
35.18
31.14
27.94
30.31
31,66
32.58
27.25
33,71
37.45
LaiRessoee-LoRRt((uuss)))
40.22
34.92
32.73
29.70
36.65
34,85
30,80
27.84
30,20
31,58
32.46
25.00
33.54
35,32
simple to complex scenarios. In addition, comparisons on real-world image restoration datasets (Tab. 3) and unseen degradations (Tab. 4) reveal that our method also guarantees better generalization performance than others. We found that it becomes challenging for all-in-one models to achieve significant performance gains over SOTA task-agnostic methods as the number of restoration tasks increases, suggesting that the use of tailored prompts expert modules may have limited impact during training. This can be attributed to the used trivial degradation representations, which may fail effectively guide methods to learn different restoration process. More comparisons on real-world and unseen degradations can be seen in Tab. E of the Appendix.
Comparison with Single-Task Models For fair comparison, we evaluate the performance of our method in instruction mode (Ours?). The results in Tab. 5 show that our method achieved competitive or better performance compared single-task models across various image restoration tasks. The main reason lies in the fact that when single-task models are required to handle a broader range of degradation patterns (e.g., all levels of snow), their performance on specific degradation (e.g., large snow) tends to be not guaranteed. Our method can learn image degradations in finer- mner-grained levels, allowing each expert to focus on specific degradation (e.g., different levels of snow). As a result, our approach can achieve competitive or better performance compared to single-task models.
Overhead Comparisons We calculate FL.OPs and latency with 512 x 512 x 3 input, on a single NVIDIA A6000 GPU, As shown in Tab, 6, thanks to mixture-of-experts, the overhead of our method is slightly higher than the used image restoration backbone, Restormer (Zamir et al., 2022).
Since our method is flexible, we can replace restoration network backbone with more efficient one for fitting computational cost requirements. We replace the used Restormer with lightweight NAFNet (LiteNAF.) and lightweight Restormer (LiteRes.), the performance and training cost comparison is reported in 3-task all-in-one image restoration task. As shown in Table 7, inference speed and lower training cost than PromptIR, and its restoration performance outperforms
achieves fast
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Table 6: Overhead comparisons SOTA methods.
Methods
iPParmss (M)
#FLOPs (G)
Latency (8)
Restormer (Zamir et al., 2022)
26
1128.9
0.368
NAFNet (Chen et al,, 2022)
17
505.5
0.186
PromptR (Potlapalli et al,, 2023)
36
1266.2
0,355
AirNet et al., 2022a)
9
2414
0,548
DCPT-PrompüR (JiaKui et al., 2025)
36
1266.2
0.355
MoCE-IR (Zamfir et al., 2025)
25
716.16
0.347
UniRestorer (Ours)
627
1155.8
0,484
UmiRestorer-LoRA (Ours)
171
1155.8
0.484
Table 7: Performance and overhead comparison to PromptIR with our lightweight variants in three- task all-in-one image restoration setups.
Methods
Derain Rain100L
Dehaze SOTS
Denoise BSD68
IFLOPs
Latency
Params.(M) Inf. Mem.
Timeth)
(G)
(8)
Inf/Train
(M)
Train
PromptR (Potapalli et al., 2023)
36.37
30.58
31.12
1266.2
0.355
39.6/39.6
3552
201
Ours
41.68
36.44
31.43
1155.8
0.484
26. 1/339.
4258
578
Ours-I LitNAF.
38.98
31.69
31.28
154.7
0.156
17.1/22..4
1818
169
Oute-LiteRes.
41.41
35.38
31.35
672.7
0.395
14.8/192.5
3526
442
PromptIR in the 3-task. This demonstrates that the variant of our method not only improves efficiency but also maintains superior restoration performance, highlighting that can change the restoration backbone to fit the requirement of training cost in practice.
4.3 ABLATION STUDY
Since the effectiveness of our method reflects on two aspects: in-distribution (in-dist.) and out-of- distribution (out-dist.) performance, we train and evaluate our method on synthesized low-quality data, where degradation parameters can be adjusted to determine whether the input samples fall into the in-dist. or out-dist Specifically, we train our method on DIV2K (Agustsson & Timofte, 2017) and Flickr2K (Lim et al., 2017) datasets with hand-crafted degradation pipeline, the details can be found in Sec. B. We conduct comparisons on DIV2K-valid and Urban 100 (Nah et al., 2017b) datasets, the results are in Tab. C and Tab. D of Appendix, respectively. For quick evaluation, only evaluate the performance on Urban 100 dataset in ablation studies.
Effect of DR Fineness By utilizing Ga and clustering input samples into different numbers of DR groups, we investigate the effect of DR fineness. As shown the upper part of Tab. 8, the performance in-dist. consistently improves grouping into finer-grained DR groups However, due to the increased difficulty of routing, the performance gain plateaus when #DRs exceeds 8. For performance on out-dist., clustering DR groups in proper coarse-grained level performs better than finer- mherggrinedd levels, suggesting that it is essential to combine multiple granularities to keep high performance on both in-dist. and out-dist.
Effect of Multiple Granularities Utilizing both Gu and Gg. we investigate the effect of different combinations of granularities. As shown in the lower part Tab. 8, incorporating both fine-grained levels and boarse-grained levels can maintain much better restoration performance and generalization ability than employing single granularity. Taking performance, training cost, and learning difficulty into account, we finally adopt {1, 4. 8) in our mixed mixed-degradation experiments.
Effect of Granularity Estimation- baaseedtoo---aedd Routing By comparing with 'Vanilla' method, which only routes in the finest level, we investigate the effect of granularity estimation-based routing. Specifically, we normalize degradation parameters in range from in-dist. to out-dist. into the interval [0, 1]. Then, we uniformly sample 15 points in this interval. At each sampled point, we add corresponding degradations to images in Urban 100. We compute the average granularity level and average PSNR for each point, and conduct comparison to the 'Vanilla', The visualization of the results is shown in Fig. 5. The results show that, as the degradation shifts from in-dist to out-dist, our method can adaptively select experts in coarser-grained level, while the 'Vanilla' baseline only uses experts in

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Published as a conference paper at ICLR 2026
Table Ablation study on the effect DR fine- ness and the number of granularties.
24
Aueroge PES LS01
MiO (Average)
Routers
# Levels
DRs
To-dist. Out-dist.
22.06
22.42
17.51
Gu
Cranuaarty Steniaal
8
24.22
18.35
Granuartty (Ourt
PSMS (Hamila)
16
24.30
18.44
PSWR (Durs)
[18]]
24.27
18.86
0.0
0.2
04
4,8
24.44
19.06
Oegradation parameter latter norm) varios
Gu. S3
Figure 5: Visualization of routing results on gran- ulairty and PSNR.
1,4, 8,
16)
24.41
19.47
the finest-grained level. Benefiting from coarser-grained experts, our method can generalize to a wider range of degradations, achieving better generalization performance in our-dist. while maintaining restoration performance in in-dist..
Effect of Loss Functions We conduct three experiments to validate the effectiveness of the used loss functions. By using only & loss, our method achieves 24. 14 dB on in-dist. and 18.37 dB on out-dist.. Benefits from Ldg. our method improves the performance improves performance on out-dist to 18.76 dB. Adopting Lload, the method further has 0.11 dB and 0.29 dB improvement on in-dist. and out-dist,, respectively. The results show that Ldg and Lloud can help to avoid the routing collapse to the finest- finest-grained level, particularly improving performance on out-dist. The details can be found in Tab. H of Appendix.
in-dist. to 24.35 dB and
We provide quantitative analysis of the clustering step in Tab. B. For a fairer comparison, we also train scaling-up SOTA models, and the results are in Tab. We provide more analysis about our method, including the effect of DR extractor ( Tab. G), the used loss functions (Tab. H), the effect of input resolution (Tabs. J and I), discussion on clustering method and the hybrid usage (Sec. E). Finally, more visual results can be seen in Sec. H.
Others
4.4 ANALYSIS ON ROUTING
We investigate the finest-level clustering results of CDD-11 dataset. The dominant degradation types of the eight clusters are: (1) (Haze+Rain (H+R)), (2) (Rain (R)), (3) (Lowlight+Rain (L+R), Lowlight+Haze+Raae
(L+H+R)), (4) [Lowlight++aze (L+H)), (5) (Haze+Snow (H+S), Snow (S)),
(6) (Haze (H)), (7) [Lowligh++Haze-Sin We further visualize the routing results in CDD-11 testset. The routing results in Fig. D of the Appendix show that when degradation is ambiguous, the accurate degradation estimation becomes difficult, our method prefers coarser-graid -rrained granularity level to guarantee the expert network can handle the input; when degradation estimation accurate, our method mostly selects the finest- grained experts for superior performance. More results on routing can be seen in Sec. F of the Appendix.
(L+H+S), Lowlight+Snow (L+S)), (8) (Lowlight (L)).
CONCLUSION
In this paper, we introduced our UniRestorer, novel universal image restoration framework designed to leverage degradation priors for improved restoration performance while alleviating the inevitable error in degradation estimation. To this end, we learn all-in-one image restoration in multiple granularities and propose to estimate image degradation at proper granularity. Specifically, perform hierarchical clustering on degradation space and develop a grantiigr degradation set well MoE restoration model. Besides vanilla degradation estimation, granularity estimation is further introduced to indicate the degree of degradation estimation error. By jointly utilizing degradation and granularity estimation, train routers adaptively allocate unknown corrupted inputs to the most appropriate expert. Experimental results demonstrate our superior performance and promising potential in closing the performance gap to specific single-task models.
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Acknowledgements. This work partially supported by the National Key RD Program of China under Grant No. 2022YFA1004100, China Postdoctoral Science Foundation under Grant No. 2025MM78337,, and the National Natural Science Foundation of China (NSFC) under Grant No. 62371164.
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APPENDIX
The content of the appendix involves:
Theoretical analysis on granularity estimation is in Sec. A.
Datasets, degradation pipeline, and degradation parameters are in Sec. B.
Implementation and training details are in Sec.
More comparison results are in Sec. D.
More ablation studies are in Sec.
More analysis on routing are in Sec. F.
Limitation and future works are in Sec. G.
More visual comparisons are in Sec. H.
A THEORETICAL ANALYSIS
Existing degradation- aware routing methods typically generate a deterministic degradation repre- sentation E I for the i-th sample and use it to determine the expert assigned to that sample. However, practice, it challenging to model an accurate degradation representation, e,. Inaccurate representations of can lead to the selection of the wrong expert, which severely impacts the restoration performance. Motivated by this issuc, in this paper, we utilize a distribution MV,,, of instead of a deterministic representation to model the degradation, where μ4, = (c, E RD and of (e,) RD represent the estimated degradation and uncertainty, respectively. and I is the identity matrix. In our implementation, we learn branch Hy to estimate 6, and learn Hg to estimate o,- Given the distribution N (11;; I and & degradation cluster centers u = [u,.... Uy, " )
we can calculate the probability i-th sample belonging to j-th cluster follows.
(u, 11:) 2
plu,WWiu ofI)
exp
(A)
v 2TTOz
20²
Then the log likelihood is as follows,
1
In Inp(u,)AV(,, oi Taking the negative of Eq. (B) and ignoring the constant term, obtain the used loss function as follows.
20? uu
m1)2
In of
In 1n
(B)
2
1
Leg
20 20?
(u,- + +22 Ino?
(C)
In Eq. (C), the first term encourages the degradation estimation μt to be close to its corresponding degradation cluster center uj. The second term serves as the regularization, preventing the estimation uncertainty from becoming too large.
Thus, we can conclude that, for samples with easily estimable degradation, (u,- is usually small, and of could also be small; for samples with complex or ambiguous degradations, (u, 24, is large, leading to a larger of being learned to balance the loss value. Based on the degradation estimation μi; and the estimated uncertainty of we construct degradation-basee routing and granularity based routing. Specifically, when the model produces a degradation estimation μ4; with a small uncertainty of. our model tends to utilize the finer-grained expert deal with this sample, ensuring restoration accuracy. In contrast, when the model produces a degradation estimation (4; with a large uncertainty of. it chooses the coarser- coarser-graines grained expert to deal with this sample, promoting restoration generalization. This design effectively mitigates the issue restoration failure caused by wrong routing in the existing degradation-awee routing methods.
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Clean Inputs
Lowlight
Blur
Noise
Rain
Snow
Haze
Low-quality Outputs
Figure A: Degradation Pipeline.
B DATA
B.1 DATASET
For quick lookup, we list the experimental setups for all reported results Tab. The detailed information of experiments on single-degradation, mixed-degradation, single-task comparison, and ablation studies as follows: (1) Single-degradation: For training, we adopt Rain 100L (Yang et al., 2017) dataset for deraining, RESIDE (Li et al., 2019) dataset for dehazing, DFBW (DIV2K (Agusts- son & Timofte, 2017), Flickr2K (Lim et al., 2017), BSD (Martin et al., 2001), and WED (Ma et al., 2017)) datasets for Gaussian color image denoising and compression artifacts removal, GoPro (Nah et al., (0) dataset for motion deblurring, LOLv1 (Wei et al., 2018) dataset for low-light enhance- ment, Snow100w 100K (Liu et al., 2018a) dataset for desnowing. Following (Potlapalli al., 2023; Zhang et al., 2023a), we evaluate our method on Rain 10LL (Yang et al., 2017) for deraining, SOTS (Li et 2019) for dehazing, BSD68 (Martin et al., 2001) for Gaussian color image denoising. Go- Pro (Nah et al., 2017a) for motion deblurring, LOLvI I (Wei et al., 2018) for low-light enhancement, 100-L00KLL (Liu et al., 2018a) for desnowing, and LIVEI (Sheikh al.) for compression artifacts removal. (2) Mixed-degradation: We adopt CDD-11 11 (Guo et al., 2024) as training and evaluation data. For more complex scenarios, we synthesize mixed degradation datasets from clean images in DF2K (DIV2K (Agustsson & Timofte, 2017) and Flickr2K (Lim et al., 2017)) datasets for training, we evaluate the performance on DIV2K-valid and Urban 100 datasets The degradation pipeline is illustrated in Fig. A. (3) Single-tast, comparison: We use the same datasets as those used in the single-degradation setup.
B.2 DEGRADATION PIPELINE AND PARAMETERS
We follow the degradation pipeline of (Zhang et al., 2022a) to construct synthetic low-quality datasets for the training of the degradation extractor and all-in-one image restoration (mixed-degradation setup). As shown in Fig. A, given high-quality clean inputs Y, we orderly add synthetic lowlight, blur, noise, rain, snow, and haze synthesize corresponding low-quality outputs X. We adopt gates between different degradations to gap one of them probably, the probability of all gates is generally set to 0.5. The way of each degradation is introduced as follows:
Blur Following (Zhang et al., 2018), we synthesize blurring degradation as,
x x=
(D)
where K is the blur kernel. In our implementation, the type of blur kernel includes isotropic and anisotropic, each of which is employed with the probability of 0.5. The kernel size is sampled the range [7, 23). The standard deviation of the Gaussian kernel is set in range [1, 3] for in-dist. and [3, 4] for out-dist.
Noise Gaussian noise is synthesized as,
X ==Y-1 N,
(E)
where N ~ NN(0 22) denotes Gaussian noise. a is set in range [15, 35) for in-dist. and [35, 50] for out-dist.
Rain Raining degradation is synthesized as,
Y=Y+R.
(F)
where R is rain streaks. Following (Xiangtao et al., 2024), R is synthesized with random noise and Gaussian blur, where noise is related to the strength of the rain, and blur kernel is related to the rain
direction, length, and width. We adopt the number of rain streaks range [50, 100) in in-dist. and [100, 150] for out-dist. Rain direction generally adopted in range [-45", 45%]. rain length is set in range [20, 40), and rain width is uniformly sampled from (3, 5, 7, 9, 11).
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Table A: Setups of all experiments. We list model, training data, evaluation data, degradation pipeline, and metrics for all reported tables. training.
in training data means directly evaluation without
Table ID
Training data
Evaluation data
Degradation Pipeline
Remark
Single degnreddggrtttiion
Tab. Tab. 5,
RainJ00L, RE- SIDE, DIV2K, Flickr2K, WED, BSD, Go- Pro, LOLvI, Snow100K
Rain SOTS, CBSD68, Go-
Tab. I: evaluation of all one image restoration performance in single- degradation setup:
Tab. F
Pro, LOLv1, Snow100K-ID
Tab. 5: comparing our performance to single-task models;
LIVE1
Tab. F: comparing to SOTA methods with parameter scaling up.
Tah. 3.
Using model same Tab. 1
LHP, L.L.v2 RealSnow, SIDD, DND, ISP artifact, Re- alBlur
This table is for evaluating the gener- alization performance on real-world datasets.
Tab. E
Tab 4.
Using model same Tab,
RainDrop.
This table for evaluating the gen- eralization performance on unseen degradations.
TOLED, UIEB
Tab. J,
Using model same Tab.
DIV2K, 4K- Rain, 4K-Haze, RVDS
Gaussian noise, jpeg compres- sion, lowlight, motionblur
These two table are for validating the resolution effect of input images the extracted DR.
Tab.
Tab. G
Rain200L, Rain200H, DID, DDN
Rain200L, Rain200H, DID, DDN
This table is for validating the ef- feetiveness of our DR extractor. To increase the diversity and complexity rain degradation, we expand derain- ing datasets.
Mixed-degrudaite
Tab.
CDD-11
CDD-11
This table for evaluating the perfor- mance all-in-one image restoration in mixeedddegradation setup.
Tab. D
DIV2K. Flickr2K
Urban100
Using pipeline Fig. A
This table is for evaluating the perfor- mance of all-in-one image restoration in mixed- -eggadddggaatttoon sctup.
Tab. C
Using model same as Tab. D
DiVlk-
Using pipeline as Fig. A
This table for evaluating the perfor- mance of all-in-one image restoration in mixed- deneddddgadatioon setup.
Tab Tab. H
Using model same as Tab. D)
Urban100
Using pipeline as Fig.
We validate the effectiveness of method on two aspects: in-distribution and out-of-distribution
Tab. 8: validating the effect of DR finess and #granulairty:
Tab. H: validating the effect used loss functions;
Haze Following (Li et al., 2019), hazing degradation is synthesized as,
X = T + (1 T(Y) = exp (=)(D((YY))
(G)
where a is the global atmospheric light, T(Y) is the transition map. D(Y) is the depth map, and is the scattering coefficient of the atmosphere. In haze degradation synthesizing, the value of / largely
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Figure B: T-SNE visualization of deep features (Restormer Zamir et al. (2022), PromptlR Potlapalli al. (2023)) and degradation representations (Ours). (a) and (b): Restormer and PromptIR are not aware of fine-grain fine-grained degradation representations. (c) While PromptR demonstrates a certain capability to distinguish between different types of degradations, it still exhibits misclassification across various cases. (d) Our method distinguish degradation representations well in both coarse- grained and fine mme-grained levels.
decides the thickness of haze. We adopt one publicly released project MegaDepth' to estimate the depth image. our experiments, the global atmospheric light is set in range [0.8, 1], the 3 set in range [0.5, 1.5] for in-dist. and [1.5, 2] for out-dist.
Snow Snowing degradation is synthesized as,
X = Y o (1- M) + M,,
(H)
where M is the snow mask randomly sampled from Snow 100K (Liu al., 2018a) dataset. Lowlight Following (Guo et al., 2024), lowlight degradation is synthesized as,
Y
X
I(Y)"
(I)
where I(Y) is the illusion map obtained from RetinexFormet (Cai et al., 2023) and y is the darkening coefficient. Since explicitly include noise degradation in degradation pipeline, we do not additionally consider noise in this step. We set y in range [1, 2] for in-dist. and [2, 2.5] for out-dist.
Besides, JPEG compression is synthesized by employing the publicly released project DiflJPEG?.
C IMPLEMENTATION AND TRAINING DETAILS
Degradation Extractor Our degradation extractor is based on previous work, DA-CLIP (Luo et al., 2024). DA-CLIP uses a lightweight image controller, a copy of the CLIP image encoder augmented with pero-initialized residual connections, to guide the image encoder output toward producing both content-related features and degradation-relate embeddings. Using contrastive learning, the content embedding is aligned with clean text descriptions, and degradation embedding indicates the type of degradation, aligned with degradation-related text prompts. For more details, please refer to the original paper. To demonstrate the effectiveness of degradation extractor, visualize the T-SNE results in Fig. B (a). For example, in the single dehazing task, both Restormer and PromptIR fail to capture fine-grained DRs, as shown in Fig. B (a) and Fig. B (b). In contrast, our method learns DRs at finer levels of granularity and allows restoration on more specific degradations, thus achieving competitive or even better performance to single-task models. While PromptIR can separate different types of degradations, it still exhibits misidentification across various cases. Such cases may render the dedicated prompts less effective, which are originally as guidance for specific tasks. As a result, PrompuR may offer limited performance improvements over task-agnostic methods in some of the restoration tasks.
Hierarchical Clustering We follow SEAL (Wenlong et al., 2024) using silhouette score (Roussceuw, 1987) to quantitatively evaluate the quality of the clustering, which is defined as follows:
b, max (as, b,) 11-1,
S
S
S
(J)
htttps://githubb.com//eee i /https://gitte thu.. cot/ imni t z FFJPNG
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Table B: Silhouette score of K-Means clustering.
Silhouette Score
(if DRs} )
Ist-level 2nd-level 3rd-level
(1. 2)
0,3103
[1. 4]
0.4926
{1, 8)
0.6605
{1, 16}
0.5932
{1,4, 8)
0.4926
0.6686
[1, 4, 8, 16)
0.4926
0.6686
0.5525
where o, denotes the average intra-cluster distance of i-th sample, and b; denotes the minimum average distance between i-th sample and all points in the nearest neighboring cluster. As indicated by Eq. (J), a higher silhouette score reflects a more compact and better-separated clustering structure. Specifically, we calculate the silhouette score in mixed-degradation setup. As shown in Table B, when the number of DR groups increases from 2 to 8, the silhouette score improves. This is because a finer partition allows degradation patterns in each group to be more consistent. However, further increasing the number of DR groups from 8 to 16 does not yield additional improvement. This suggests that overly fine-grained DR groups may introduce redundant or noisy partitions, leading to less meaningful separation and consequently limiting the clustering quality. Besides, the setting of {1, 4, 8) produces higher silhouette score than that of {1, 8). This indicates that introducing an intermediate degradation level helps the model better capture the continuous transition among degradation patterns, leading to more compact gura-group structures and clearer inter-group separations. In addition to the silhouette score, the ablation study in Tab. 8 of the main paper also indicates that {1, 4, 8} is better trade-off between performance and training cost.
Multi-Granularity MoE Restoration Model After training the degradation extractor, we construct the DR set for all training samples. Then, we hierarchically cluster the obtained DR set into multiple groups in different granularities, constructing version, considering the task agnostic capability, we generally adopt Restormer (Zamir et al., 2022) as the expert model across most tasks. However, we found Restormer cannot perform well in low-light enhancement. We thus observe clusters predominantly composed of low-light degradations and select RetinexFormer (Cai et al., 2023) as the expert model for those clusters. In our LoRA version, we adopt Restormer as the base model and generally train LoRA weight with rank of 8 as (Ai et al., 2024a). Referring to the total number of degradation types and degrees training DR extractor, adopt levels and empirically set 1, 7, and 19 clusters for the 0-th level to the 2-th level in single-degradation all-in-one setting. For mixxeddderaddttion setup, we generally adopt 1, 4, and clusters, which performs well in both CDD-11 1 benchmark dataset and our introduced more complex scenarios, indicating good generalization ability.
craliiggrallrrityy degradation sets. In our full-model
Structure of H and G Without dedicated design, adopt 5-layer MLP networks for both H. and Hg. in which the number of input and output channels is 512 (the same as the dimension of DR e). The structures of Gu and Gg one-layer MLP networks with input channels of 512 and the number of output channels equal to the number of experts.
Instruction Mode, Ourst The instruction mode of our method allows inference based on given task instructions, i.e., the task name. In our implementation, the task instructions can convert to specific binary masks, which represent pruning not pruning on the corresponding expert model in our MoE restoration model. According to the masks, we first prune out the corresponding expert models, then we conduct degradation- and granularity comrppted inputs. In this way, the emror in degradation estimation is alleviated, the routing accuracy is highly improved, and thus the restoration performance further improved.
routing find the most appropriate expert model for
Training of Degradation Extractor To train our DR extractor, we adopt the ways in Sec. B to synthesize various types of low-quality data. According to the parameter that decides the degradation degree, we separate synthetic data into multiple groups. For deraining degradation, strength in range [o, 50) is small rain, [50, 100) is medium rain, and [100, 150] is large rain; In denoising, o of Gaussian noise in range [0, 15) is small noise, [15, 35) is medium noise, and [35, 50) is large
noise; In dehazing, scattering coefficient larger than 0.1 is thick haze, 3 smaller than 0.1 is
normal haze; In deblurring, kernel size of motion blur kernel larger than 15 is large blur, kernel
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size smaller than 15 is small blur; In desnowing, we snow masks from Snow100K dataset and adopt the official separation. In compression artifacts removal, image quality in range [0, 15) is large compression, [15, 35) is medium compression, [35, 50] is small compression; In lowlight enhancement, the darkening coefficient in range [1, 1.5) is light corruption and [1.5, 3) is heavy corruption. The CLIP image encoder typically pre-processes the input resolution of 224 x 224, it has little effect on the accuracy of semantic awareness in high-level vision tasks and task-level degradation classification in original DA-CLIP. However, we found that this large-ratio distortion can lead to the loss of fine- ane-grained degradation information, e.g., noise can be heavily alleviated by downsampling CLIP processor. To address this issue, we crop high- high-resolution clean images into 224 x 224 patches and add synthetic degradations with different types and degrees to construct the dataset with fine-grained degradation, which can well maintain fine-grained degradation information. And typically, we adopt center-cropped 224 x 224 images as input in inference.
Training of Multi-Granularity MoF. Restoration Model Most works in high-level vision and NLP tasks train experts and routers jointly. To well deal with the conflicts between restoring different degradations, MoE system in image restoration works or all-in-one image restoration works prefer separate training (Zhu et al., 2023; Park et al., 2023; Cao et al., 2024b; Tian et al., 2024; Chen et al., 2024; Ai et al., 2024a), i.e., sequentially training experts and routers. Similarly to the mentioned works, our experts explicitly determined to solve degradations in each degradation set, experts should be learned in advance and are not affected by the routers to guarantee the stability of the whole model. In the training of experts, we first conduct hierarchical clustering based on collected datasets. after that, all degraded training images are divided into multiple groups in different granularities. In each gramularity, we record for each group the data samples that is allocated to it. Then, we use the recorded data to train expert restoration network for each group. After training of experts, the weights of experts are frozen, and we train routers based on degradation- and granularity- estimation. We further tried two manners to train our method: training experts and routers from scratch simultaneously. further finetuning after sequentially train experts and routers. In the former, we found that the trainnig is instable and the capacity of experts quickly collapses. In the latter, it costs additional training time and GPU memory while the improvement is marginal.
Training Details For the training of experts, we generally use the AdamW optimizer with batch-size of 8 and patch-size of 128, we take the Coneainn learning rate scheduler with the initial learning rate of 3e-4, In full-model version, experts version, we train model on the coarest DR set as the base model, based on it, we then train LoRA weights other finer DR set with rank of 8 as (Ai et al., 2024a). After training experts. the weights of experts are frozen, and we train degradation and granularity estimation based routers. We adopt Adam optimizer with batch-size of and patch-size of 256, we adopt a fixed learning rate of 1e and we set a to 0.1 and B to 0.01 in Eq. (1)), respectively. All experiments are trained on 8 NVIDIA A6000 GPUs
seperatly trained on each DR set; In LoRA
D MORE COMPARISON RESULTS
Results More Complex Mixed-degradation In Tab. C and Tab. D, we further evaluate the performance of all-in-one image restoration in mixed-degradation setup. The evaluation results show that our method outperforms previous task-agnostic methods and all-in-one methods from simple degradations to complex degradations, and it also generalizes well in out-dist. degradations.
More Comparisons on Real-world Datasets and Unseen Degradations More comparisons on real-world denoising, real-world motion-debluring and ISP artifacts removal can be seen in Table E. It shows that our method demonstrates better generalization ability compared to existing all-in-ome image restoration methods.
Comparisons to Scaling-up SOTA Methods According to the degradation- and granularity- es- timation, the routers select the most appropriate expert for each input low-quality image, leading to a sparse inference strategy (see Tab. 6). Though our method has comparable inference #parame- ters, FLOPs, and latency to methods with similar backbones, one may know the effect of training parameters. To mitigate the gap of training #parameters, we scale up the training #parameters of Restormer (Zamir et al., 2022) (by scaling up the width to 128), PromptR (Potlapalli et al., 2023) (by scaling up the width to 128), and MoCE-IR (Zamfir et al., 2025) (by scaling up the width to 128 and increase the number of experts tp 7) The experiment conducts all-in-one image restoration
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Table C: All-in-One image restoration (mixed-degradattion) results. PSNR (dB, 1) and SSIM (1) are reported in RGB color spaces. The best performance is highlighted. The evaluation is on DIV2K-valid with in-dist. and out-dlst. degradation parameters. 'TIT' means the average performance of tasks (3T: H-R, HIN. HI-N-R: ST. HI-R, H-N. HI-N-R, LL-II---, LL-II-N---;; 7T: all).
Methods
H-R
H-N
H.N-R I.-..NBR LL-.NSS B-R
LL-HNN-
LLIHNN- B-R
Average ST
ST
7T
le-distribution
MPRNNrr (Zamir et al, 2021)
17:22
.91
19.02
717
17.18
639
17:25
549
17.18
583
16.99
.466
16.80
506
17.80
17.57
17:37
SwinIR (Liagg ctal. 2021)
16.35
.60
18.74
693
16.48
.585
16.78
513
16.73
555
16.54
.144
16.10
482
17.19
17.01
16.81
NAPNet (Chen et 2022)
19.14
.775
22.71
843
18.56
.725
17.41
603
17.09
.631
16.37
.505
15.83
530
20.13
18.98
18.15
Restoree (Zamir et 2022)
19.85
20.44
820
17.64
.719
16.86
588
16.56
.623
15.92
.483
15.47
19.31
18:27
17.53
AirNet (1i et al, 2022a)
18.67
7752
23.34
855
19.03
.726
20.34
PrompilR (Valanaraso et 2022)
22.20
20.86
.772
MIDIR (Xiongtao et al., 2024)
24.58
.92
24 /6
879
22.42
818
19.46
661
18.12
561
23.82
21 80
InstractRR (Conde 202-4)
21.57
.827
23,80
838
24.14
.775
19
666
18.06
.64
22.17 20.75
OneRestore (Goo et 2024)
17.35
.769
17.48
UniRestorer (Ours)
29.63
.950
26.93
880
26.33
.875
24.29
.30
22.71
.790
21.87
.735
20.10
.729
27.63
25.88
24.55
UniRestorrr-- oRR (Ours)
29.28
.944
26.61
.833
26.03
.869
24.01
824
22.45
.784
21.63
.29
19.89
.724
27.30
25.67
24.28
Out-of Auctoof-distzin distribuction
MPRNet (Zamir et al, 2021)
15.71
.63
16.38
.598
15.57
.560
14.84
.345
14.39
.355
14.58
.286
14.33
299
15.88
15.41
15.14
SwinIR (Liang el. 2021)
15.10
15.79
.48
.41
.353
.282
13.95
298
14.88
14.66
NAFNet (Chen et ol., 2022)
16.77
.713
19.68
.751
15.11
.595
14.40
385
14.05
.300
13.89
.317
13:27
324
17.18
16.00
15.31
Restomer (Zamiz al, 2022)
17.44
.723
17.31
.738
17.44
.723
14.11
.378
13.59
.388
13.60
.314
12.83
318
17.39
15:97
15.18
AirNet (Li cta 2022:)
17.50
.732
20.16
.277
15.07
.3800
17.57
Promptl (Valmnrrasu et all, 2022)
19.84
7797
20.55
795
18.22
.672
19.54
MiOIR (Xiangiao al., 2024)
20.85
22.39
.066
InstrctlR (Coade et al., 2024)
20.33
.225
19.02
.36
17.74
.397
15.34
386
14.52
296
19.03
17.39
OneRestrre (Gio et al, 2024)
17.81
.732
16.77
580
15.10
.595
17.49
566
21.04
.682
16.56
UniRestorer (Ours)
27.32
23.82
23.42
.801
.661
20.15
.631
.529
18.03
22.45
21.49
UniRestorer-LoRA (Ours)
26.96
.29
23.47
801
23.09
7792
19.92
.651
19.86
.622
17.24
(522
17.74
.535
24,51
22.66
21.18
Table D: All-in-One image restoration (mixed-degradatiin results. PSNR (dB, 1) and SSIM (1) are reported in RGB color spaces. The best performance is highlighted. The evaluation is on Urban 100 with in-dist. and out-dist. degradation parameters. 'nIT means the average performance of n tasks (3T: H-R, H-N. H-N-R: 5T: H-R, H-N, H-N-R, I.I.H-N-R, I.L-H-N---;; 7T: all).
Methods
HNN- LL-INN.R LLMBNNS
LLM.N. B-R
LLM.N.. B-R
Average ST
3T
TT
In-distriburiin
MPRNNtt (Zamir 2021)
17.30
.784
19.78
.812
18.55
.749
17.00
589
15.02
555
16.17
.169
16.64
580
18.54
17.53
17.20
SwintR (Liang et atal.,220)
16.25
.61
20.02
770
17.52
.622
16.47
543
16.68
.70
15.97
.881
14,42
379
17.93
17.38
16.76
NAFNet (Chen et al., 2022)
19.79
20.08
569
.64
.457
.099
19.31
17.57
Restomee (Zamir al, 2022)
19:40
.007
22.82
846
18:06
.727
16.41
.604
16.67
.636
14.62
.43
14.14
367
20.09
18:67
17.4
AinNet (Li al, 2022a)
17.93
7769
20.85
821
15.40
.676
18.06
PrompilI (Valanarasu et all, 20222
21.46
860
27.3
916
19:53
780
22.76
M:OIR (Xiangtao et al., 2024)
28.98
.945
27.34
906
23.68
840
19.43
711
16.49
.25
26.66
OneRestore (Gso et al. 2024)
17.58
.771
17.65
.764
18.39
.712
18.39
746
19.73
.724
17.87
18.34
MoCE-IR (Zamfir al, 2025)
19:27
.824
17.81
819
713
547
17,23
17.85
UniRestorer (Ours)
27.68
9943
33.66
.20
26.40
896
25.54
844
22.31
.00
20.61
.698
18.28
.607
27.58
26.11
24.21
UniRestora--ooRA (Ours)
27.33
28.34
.908
26.
890
25
.838
.794
27.26
25.82
23,93
Oni-of-diatribnine
MPRNet (Zamir et 2021)
15.80
.639
14.13
630
.64
369
11.65
266
.289
13.92
15.28
14.47
14.29
SwinIR (Liang et al. 2021)
15.63
.610
13.96
.581
13.58
.455
13.86
.37
12.63
.22
12.81
.217
12.20
264
14.39
13.93
13.52
NAINee (Chen et al., 2022)
15.93
.330
15.86
.772
15.36
.688
14.07
305
12.90
340
13.47
201
13.25
281
15.71
14.28
14.40
Restomner (Zamir 2022)
16.45
364
.313
.222
16.00
14.47
AirNet (Li at,, 2022a)
16.60
.735
17.56
217
13:75
.572
15.97
PrompiIR (Valanarasu et al., 2022)
20.93
.822
21.85
812
16.52
.647
19.76
MiDIR (Xiangtao et 2024)
21.75
736
19.76
.712
453
.287
20.44
OneRestrre (Gio et al, 2024)
16.58
7720
16.76
.741
15.46
662
16.01
679
18.03
612
16.26
16.56
MRCT-IR (Zamfir al,, 2025)
18.72
.826
14.76
.705
11.32
.585
17.93
592
19.01
.64]
14,93
16.34
Unikestorer (Ours)
20.65
8856
20.10
766
20.48
.755
19.42
.585
18.43
.542
16.97
.419
17.09
.400
20.41
19.81
19.02
UniRestorer-Late LoRA (Ours)
20.29
848
19.75
.753
20. 15
.746
19.13
575
18.14
.533
16.72
.412
16.80
.82
20.06
19.49
18.71
single-degradation setups, As shown in Tab. F, even after scaling up training parameters of Restormer, PrompuR, and MoCE-IR same to ours, our method still achieves better performance across all tasks, it reveals that simply increasing the model size is not the essence of improving the performance, which is different from the scaling law in high-level vision and NLP tasks. From this perspective, our method provides an effective paradigm for scaling up low-level vision image restoration models, enabling both improved performance and efficient computation.
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Table Comparisons SOTA methods on more real-world degradations. PSNR (dB, f) is reported in RGB color spaces.
Methods
SIDD
DND
ISP artifact RealBlur-J
RealBlur-R
PrompiTR (Potlapalli et al,, 2023)
24.32
25.19 19
12.81
25.48
33.34
MiOIR (Xiangtao al., 2024)
22.87
23.67
13.79
24.51
16.79
MoCE-IR-3T (Zamfir et al., 2025)
23.91
24.04
12.97
25.60
31.39
MoCE-IR DOCE--R-ST (Zamfir et al., 2025)
15.90
21.16
12.88
14.30
12.20
Ours
25.10
26.47
16.26
26.10
34.06
Table F: Comparisons to SOTA methods by vanilla scaling up. PSNR (dB, 1) and SSIM (t) are reported in RGB color spaces. The best performance is highlighted.
Methods
Params
Derain Rain100L
Dehaze SOTS
Denoise BSD68
Deblur GoPro
Lowlight LOLvI
Desnow Snow100K
CAR LIVEI
Average
Restormer (Zamir all,, 2023)
183M
34.46
.981 27.83 958 31.10
.96
27.78
.891
22.03
.788
26.98
993
26.88
.865
28.15
PromptR (Potlapalli al., 2023)
202M
34.94
.94
27.59
952
31.18
933
27.77
888
21.44
810
27.08
923
26.85
863
28.12
McCE-IR (Zamfir et ul., 2025)
200M
37.70
.985
29.93
962
31.30
929
29.61
.914
22.42
790
28.58
939
27.07
.837
29.51
UniRestorer-Lakk (Ours)
171M
40.22
.911
34.94
971
31.39
937
30.86
.933
24.42
827
30.53
925
30.31
922
31.81
UniRestorer (Ours)
627M
41.68
.966
36.44
.944
31.43
.936
31.48
.988
26.67
.863
31,12
940
30.57
9/6
32.77
Table G: Effect of DR extractors.
Table H: Effect of loss functions.
Rain
Rain
Test 1200
Test
Methods
200H
200L
2800
E
Lay Llood
MiO (Average) In-dist.
Out-dist.
VGG
31.47
40.59
35.26
34,18
DDR
30.97
40.37
35.25
34.17
24.14
18.37
DA-CLIP
31.65
40.67
35,24
34.12
V
1
24.35
18.76
Manual
3188
41.43
35.32
34.23
V
24.46
19.45
Ours
32.01
41.61
35.50
34.40
Table I: Effect of resolutio inputs on DR. Accuracy of degradation classification is reported. 'S', 'M',
and 'L' mean small, medium, large degree of degradations.
Methods
Derain M L
Dehaze
Denoise
Deblur
Lowlight
Desnow
CAR
Averuge
L.
S
M
L
L
$
L.
$
M
L
S
M
L
Resize
0.84 0.73 0.86 0.73 0.75 1.00 0.12 0.30 0.81 0.74 0.92 0.94 0.70 0.73 0.66 0.86 0.36 0.58
0.72
Center crop
0.92 1.00
0.93
0.96
0.85
0.83
0.91
Full-resolution
0.93 0.93 0.96 0.94 0.93 0.96
0.91
0.93
0.95
0,90
0.91
0.96 0.92 0.86
0.93
0.89
0.96
1.00
0.93
Table J: Effect of high-resolution inputs on DR. PSNR is reported in RGB color spaces.
Methods
Derain
Dehaze
Denoise
Deblur
Lowlight Desnow
CAR
Average
Resize
22,84
25.15
29.37
34.59
24.11
29.92
26.57
27.50
Center-crop
23.60
25.37
30.28
35.30
24.61
30.08
27.75
28.14
Full-resolution
23.55
25,43
30.28
35.48
24.67
30.19
27.76
28.19
E MORE ABLATION STIDUES
Effect of Degradation Extractor As shown in Tab. G, we adopt pre-trained vgg16 (Simonyan Zisserman, 2015), pre-trained ESRGAN (Liu et al., 2022b) on bicubic 4x super-resolution, and pre-trained DACLIP (Luo al., 2024) as DR extractors. sets by leveraging DRs extracted from different DR extractors. Then we re-train expert networks based on each obtained degradation set. The experiments are conducted on deraining task. Since our degradation extractor is aware of finer-grained DRs, the clustered training data can be more consistent in DR, thus our method can achieve better performance.
construct multi-granularig degradation
Effect of Loss Functions As shown in Tab. H, both Ldg and Lload benefit more accurate routing. In particular, Lag and Lload can avoid the routing to collapse to the finest- grained level, improving performance on out-dist.
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Effect of Resolution of Input Images As shown in Tab. I and Tab. J, we further compare two other manners for degradation extraction from lua-quality images. (1) We resize the full image to 224 x 224 and feed the resized image into DR extractor, named 'Resize'. (2) We directly feed the full-resolution image into DR extractor, named 'Full".
We draw several observations. (1) 'Resize' negatively affect degradation representation, since destroys the degradation contents. For example, the noise level would be suppressed after resizing an image with large noise. This is why the classification accuracy in large noise only 30%. (2) 'Full' is indeed better than our cropping strategy in restoration performance and classification accuracy of spatially variant degradation (e.g.. snow). But the overall average improvement is insignificant, within 0.05dB PSNR and 2% accuracy. (3) Our strategy has less inference time than 'Full'. For example, when testing a 3840 2160 image using DR extractor, 'Full' costs 1740ms while we only require 30ms. In this case, we achieve 58 times faster.
In summary, when the low-quality input very large, our strategy achieves comparable overall results with full-image inference in both restoration performance and classification accuracy, while offering faster inference speed. It shows our strategy can alleviate the influence of the high-resolution, and has small overall effect on the accuracy of the DR extractor.
Table K: Silhouette score of Spectral clustering in different (# DRs). Higher scores indicate better clustering.
Silhouctte Score
{# DRs }
1st-level 2nd-level 3rd-level
{1, 2}
0.3824
[1,
0.4630
{1, 8)
0.5895
{1. 16]
0,2779
(1.4. 8)
0.4630
0.6668
1.4. 8. 16)
0.4630
0.6668
0.4889
Effect of Alternative Clustering Method As shown in Table K and Table L, we conduct addi- tional experiments with a variant of our proposed model, denoted as Oursspe. where the K-Means clustering replaced with spectral clustering. We report both clustering quality (i.e., silhouette score) and restoration performance (i.e., PSNR) of the OurSspe: as shown in Table L. and Table K, respectively. For quick evaluation, the trained model only includes results for two hierarchical layers, i.e., (#DRs)=(LL 4). The results demonstrate that Oursepe achieves similar performance to Ourskms in both the silhouctte score and PSNR. This suggests our model is robust to alternative clustering methods.
Effect of Top-k Router In our experiments, we adopt the top-1 routing strategy for the following reasons: (1) Our model with top-1 routing strategy achieves inference costs comparable other methods. (2) Using additional routers, such as the top-2 routing strategy, would double the inference
cost. (3) This sparse activation strategy has proven effective in current L.M//MLMM. For example, DeepSeek-RR R1 (Guo et al., 2025) has total of 671B parameters, but only 37B parameters (~ 5.5%) are activated in a single inference, significantly reducing computational costs. We believe this strategy is also reasonable and acceptable in image restoration tasks.
Discussion on Adaptive Clustering We conduct experiments on adaptive clustering. Similar to GRIDS (Cao et al., 2024b), we offer a candidate list of [2, 3, 4, 5, 6] for cluster count & and use the binary search strategy search for the best in splitting each cluster. During splitting, we adopt the silhouette score as the metric to decide whether to split and to evaluate if the selected k is appropriate for the current split, Additionally, we ensure that the minimum number of samples in each cluster is no lower than 200. This process results in a three-level tree with hyperparameter [1, 6, 13). To evaluate the effectiveness the results of adaptive clustering, we train our method in the newly formed degradation space and evaluate the performance CDD-11 dataset. The results in Table M show that adaptive clustering indeed further improves the performance our method.
Discussion Hybrid Usage When provide perceived degradation type and degree of an image, they can also provide a confidence level (e.g.. 0, 1, 2, higher values indicate higher confidence) for the current degradation, which can as granularity routing cue. We randomly synthesize
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Table Performance comparison spectral clustering on CDD-11 dataset. PSNR (dB, reported RGB color spaces.
Methods
H
R
L-H
L-R
L-S
H-R
H-S
L-H-R
L-H-S
Avg.
OurSape
26.91
27.41
32.77
32.84
25.62
86
24.79
26.25
24.19
23.87
23.77
26.75 26.87
OurSams
27.10
27.36
33.33
32.55
25.53
25.74
25.16
26.42
24.21
24.03
24.17
Table M: Performance (PSNR) comparison of adaptive clustering on CDD-11 dataset, PSNR is reported.
Methods
L-I1
L-R
L-8
II-
11-8
L-II-
L-II-S
Avg.
OurSodaptioe
27.76 34.71 35.76
37.35
27.32
27.08
26.90
31.86
31.02
26.14
26.38
30.20
Ours
27.71 34.53 36.07
36.84
26.88
26.93
26.73
32.02
30.82
25.78
25.87
30.02
Table N: Comparisons of different hybrid usage. PSNR (dB, 1) SSIM (1) are reported RGB color spaces.
Method
'Deg.'
'Deg. & Gran.'
'Single-task'
Deraining 32.18/9973
32.42/.975
32.62/.980
1.0
2.-
Sevel
2--
100
Sample 13
(a) Routing confidence of samples in urban 100 with in-dist degradattio parameters
Sample
(b) Roating confidence of samples urban 100 with out-dist, degradation parameters
Figure C: Visualization of routing results and routing confidence on granularity. The samples are from Urban 100 dataset with mixed degradations.
rain streaks varying intensities on clean images from the Urban 100 dataset. We then evaluate the performance when both degradation type, degree, and confidence are provided (referred to 'Deg. Gran.".. Specifically, we compare the 'Deg. & Gran. with two other conditions: 'Deg." (which only provides degradation type, described in main paper) and 'single-tase taake-takk' (Restormer trained on deraining task). Results in Table N show that it has potential further improving the performance and closing the gap with single-task models when granularity cues provided.
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Table O: Effect of errors in granularity estimation. The PSNR and the number of samples allocated each granularity are reported.
In-dist.
Out-dist. Ind-level 2rd-Ievel PSNR
Methods
Ost-level
Ind-level 2rd-level
PSNR
Ost-level
Ours (actual)
5
17
78
24.46
34
50
16
19.45
Ours (GT)
13
80
24.53
29
46
25
19.66
4-
0.00
0.00
0.67
0.8
R-
0.85
0.00
0.03
0.00
033
0.00
0.78
0.2
02
0.03
0.05
0.02
0.6
Degracitoon hype
L+H
00
0.40
15
0.29
01
0.14
0.00
8-
0.07
0.03
006
300
0.20
0.64
0.11
0:03
Degredarion Type
L+H
021
011
6.68
0.4
5-
H-
5.00
0.50
0.00
0:43
0.00
0.42
0.00
0.01
0.3
L+5
0.29
4.60
0:00
0.10
0.50
0:00
H+R
0.4
0.77
0.59
L+5
0.23
0.00
0:00
0.14
0.1
5.00
0.80
0:40
6602
0.000
6.00
0.00
6.99
H+5
0.77
0:56
-.2
[H+R]
[R]
(H- 5)
(LIHIS, L-5)
L-H+R
L4tte5
0.36
0.30
0.54
Dominated Degradation Types
Grenuiariy
Figure D: Visualization of routing frequency of degradation estimation-based routing and granularity estimation- based routing. In the left figure, the horizontal axis represents the dominated degradation set of each finest-grained leaf node, and the vertical axis represents the of samples with different degradation types in the CDD11 dataset. In the right figure, the horizontal axis represents the granularity level, and the vertical axis represents the set of samples with different degradation types in the CDDI1 test dataset.
F ANALYSIS ON ROUTING
Effect of Errors in Granularity Estimation We first define the "GT granularity' the granularity level at which the model achieves the best restoration performance, compared to all other granularity levels. To assess the effect of routing noise and errors, we compare the restoration performance of our model using the 'GT granularity' and the estimated granularity. Table O illustrate the restoration performance in-dist. and out-dist., respectively. The restoration performance gap between the estimated granularity and 'GT granularity' is minimal. This suggests that granularity estimation has only minor errors, making slight impact on restoration performance. The corresponding routing confidence of in-dist. and out-dist. can be seen in Fig. C.
G LIMITATIONS AND FUTURE WORKS
On the one hand, the training data mainly comes from publicly released datasets, as required for fair comparison with baseline methods. However, the scale of training data is essential, particularly in developing large-scale models. In image restoration works, the scale of training data is often limited, e.g. Rain 100L (Yang et al., 2017) contains only 200 pairs of training data. Previous works (Wang et al., 2021) have exploited the benefits brought by scaling up the training data, we thus plan to collect more high-qualit training data bring further improvement to our method. On the other hand, we plan to expand and align our degradation space with real-wont scenarios, aiming to restore real-world image degradations.
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NAPNet
Restormer
AirNet
PrompuiR
InstrucllR
Ours
UO
NAFNet
Restonner
AitNel
PrompiRR
InstructRR
NMFNel
Renterter
ArNNt
PrompiiR
InétruckR
Purs
PRANNet
Restnmerr
SirNot
Plompii
InstraetiR
Outs
NAF Not
Restorma
AieNe
PrompliR
InstrwelR
Outs
LO
NAFNet
Ri-atomer
AitNet
PimppLR
InstructiR
Ours
WAFNet
G entormer
@ compiR
struct|R
Ours
Figure E: Visual comparison of results on All-in-One image restoration (deraining).
H MORE RESULTS AND VISUAL COMPARISONS
We show more visual comparison results in this section. Compared to task-agnostic methods, all-in- one methods, and specific single-task models, our method can restore more clear results with detailed textures. The single-degradation results of all-in-one image restoration comparisons can be seen in Fig. to Fig. The comparison results with specific single-task models can seen in Fig. L to Fig. Q. In the evaluation of mixed degradation, since the resolution of images from DIV2K-valis is too high, limiting inference on the whole image for all methods. We first crop high-resolution images into overlapped 512 512 crops, after inference, crops back to one whole image, though averaging the overlapped area, the border artifacts cannot be avoidable. Comparison result of in-dist. and out-dist. can seen Fig. and Fig. S, respectively.
inverse the cropping process and recompose
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LQ
NAFNet
Restormer
AirNet
36
3
36
36
PromptiK
InstruetlR
GT
LQ
NAFNe1
Restonher
Ait
Prompt/R
InstmettR
Ours
GT
NAFNer
Restormer
Prompe
InstruarRR
Outs
NAFNet
Restonner
Al Ne
PromppUR
Instroet
Curss
LO
NAINee
Restormer
AirNet
PrompliR
Instroul
Ourr
G1
10Q
NAP Net
Restomeer
AieNel
PronpdR
Instr netlk
Outs
t7
NAFNE
Restomerr
AurNel
RomppiR
Jestrct
Ours
GT
Figure F: Visual comparison of results All-in-One One image restoration (Gaussian coaussian-demossing))
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LQ
NAFNet
Restormet
Net
PrompdR
InstraciR
LQ
NAFNet
Restormay
AirNet
PromptIR
InstructIR
Ours
CT
NAFNet
Restemer
AirNet
PromplRR
InstruttR
Ours
1.
NAFNel
Restormer
AirNet
PromprIR
InstricI|R
GT
Figure G: Visual comparison of results on All-in-One image restoration (dchazing).
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MPRNa
Restemerr
Instricc
Ouus
IQQ
SwindR
MPRNet
NAPNet
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Figure H: Visual comparison of results on All-in-One image restoration (motiin-debbuuringg)
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Figure I: Visual comparison results on All-in-One image restoration (desnowing).
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Published as a conference paper at ICLR 2026
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Figure J: Visual comparison results on All-in-One image restoration (compression artifacts removal).
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Published as a conference paper at ICLR 2026
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Figure K: Visual comparison of results on All-in-One image restoration (lowlight enhancement),
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Published as a conference paper at ICLR 2026
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Figure L: Visual comparison results with specific models (deraining).
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Published as a conference paper at ICLR 2026
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Figure M: Visual comparison of results with specific models (Caussian-denoising)
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Published as a conference paper at ICLR 2026
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Figure N: Visual comparison of results with specific models (dehaze).
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Published as a conference paper at ICLR 2026
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Figure O: Visual comparison of results with specific models (motion-deblurring)
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Published as a conference paper at ICLR 2026
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Figure P: Visual comparison of results with specific models (desnowing).
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Published as a conference paper at ICLR 2026
NAIK.
Figure Q: Visual comparison of results with specific models (lowlight enhancement).
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Published as a conference paper at ICLR 2026
RAPN
Figure R: Visual comparison of results All-in-One image restoration (mixed-deeradaation, in-dist). Zoom in for more details.
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Published as a conference paper at ICLR 2026
DhaRtares
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Figure S: Visual comparison of results All-in-One image restoration (mixed-degradaation, out-dist). 7oom in for more details.
41


Reviews:
[{"review_id": "kS6MPjoJI7", "reviewer": "Reviewer_xgD4", "summary": "Existing all-in-one restoration schemes are either degradation-agnostic or degradation-aware, leaving a clear performance gap to single-task experts. This paper proposes UniRestorer, which first hierarchically clusters the degradation space and trains a multi-granularity mixture-of-experts (MoE) network. At inference it jointly estimates degradation type and granularity to activate the most suitable expert. Extensive experiments show that UniRestorer significantly surpasses state-of-the-art all-in-one competitors and narrows the gap to dedicated single-task models.", "questions": "Please check the weakness", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper proposes UniRestorer, the first framework that simultaneously exploits degradation and granularity estimation to overcome the inherent limitations of both degradation-agnostic and degradation-aware restoration methods.\n2. Extensive quantitative and qualitative experiments convincingly demonstrate the superiority of UniRestorer over existing all-in-one baselines and its competitive performance against task-specific models.\n3. The idea of granularity-aware expert selection is clearly articulated and technically grounded; it offers a fresh insight that could inspire future work on robust universal image restoration.", "weaknesses": "1. The paper lacks a quantitative analysis of the proposed hierarchical degradation-clustering step.\n2. No comparison or discussion is provided against alternative clustering strategies (e.g., the spectral clustering adopted in SEAL).\n3. It is unclear whether the Restormer baseline in Table 1 was re-trained under exactly the same degradation protocol and parameter budget; an ablation that removes both degradation and granularity estimation while keeping the backbone capacity fixed would better isolate the gain of the proposed method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ODQYicWArj", "reviewer": "Reviewer_TgnW", "summary": "UniRestorer proposes a multi-granularity MoE framework for all-in-one image restoration. It (1) hierarchically clusters degradation space, (2) trains multi-level MoE experts, and (3) uses joint degradation + granularity estimation to route inputs to the most suitable expert. Claims large gains over SOTA all-in-one models and narrows gap to single-task models. Code and models will be released.", "questions": "Report inference FPS on RTX 3090 for 512×512 input — how much slower than PromptIR?\nTest on real-world degradations (e.g., DND, SIDD, GoPro real blur).\nAblate dynamic clustering — can granularity be learned end-to-end without offline K-means?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Granularity estimation elegantly handles degradation estimation noise — robust and practical.\nHierarchical clustering + MoE scales well across 15+ degradation types.\nLarge, consistent gains (e.g., +2.1 dB PSNR on mixed test sets).\nComprehensive ablations (granularity levels, routing loss, expert count).\nClean figures: t-SNE of degradation space, expert activation heatmaps.", "weaknesses": "Clustering is offline and static — no online adaptation to new/unseen degradations.\nGranularity estimator adds overhead — no inference latency reported (vs. PromptIR, AirNet).\nNo theoretical justification for hierarchical clustering choice (e.g., why 3 levels?).\nEvaluation limited to synthetic degradations — no real-world camera pipeline (e.g., RAW → ISP).\nMoE training unstable? No mention of load balancing loss or expert collapse.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qMw03xkIDw", "reviewer": "Reviewer_tpRc", "summary": "The paper proposes UniRestorer, a universal all-in-one image restoration framework that bridges degradation-agnostic and degradation-aware paradigms. It performs hierarchical clustering on degradation representations to build a multi-granularity mixture-of-experts (MoE) model. Through degradation and granularity estimation, the router adaptively selects fine- or coarse-grained experts, achieving robustness against estimation errors. Experiments on single- and mixed-degradation benchmarks show that UniRestorer surpasses prior all-in-one methods and nearly matches single-task performance", "questions": "1. How sensitive is performance to the number of levels (l = 3) or cluster count per level? Could adaptive clustering improve robustness?\n2. Can you visualize which experts are selected for different degradations and how granularity affects routing under ambiguous inputs?\n3. Could the same hierarchical expert tree generalize to unseen degradation combinations(e.g., low-light + blur + noise) without retraining?\n4. In hybrid usage, can user-provided degradation cues override granularity routing? Would it further close the gap with single-task models?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper introduces a multi-granularity degradation representation that unifies coarse- and fine-grained experts. The idea of granularity estimation to quantify degradation uncertainty and guide routing is novel and intuitive.\n2. Comprehensive experiments: covers 7 single-degradation and 11 mixed-degradation settings, plus real-world and unseen tasks.\n3. Paper is well-organized and technically detailed with intuitive figures (especially Fig. 3 illustrating routing).\n4. The hierarchical MoE design could inspire cross-domain generalization and adaptive restoration architectures.", "weaknesses": "1. While granularity estimation is conceptually convincing, there’s no formal uncertainty-theoretic or probabilistic analysis of its behavior.\n2. Each granularity level adds parameters and routing complexity; actual FLOPs and latency comparisons are limited. The LoRA variant helps, but trade-offs between full and LoRA experts could be better quantified.\n3. The K-means–based clustering assumes a consistent degradation embedding space; sensitivity to clustering hyperparameters (number of clusters, feature normalization) is not reported.\n4. Evaluation primarily uses synthetic degradations; more real-world degradation diversity (e.g., motion blur, ISP artifacts) would strengthen the claim of universality.\n5. The effect of routing noise or errors in granularity estimation itself is underexplored; visualization of routing confidence would add interpretability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xUUQtmJWIn", "reviewer": "Reviewer_Vp55", "summary": "UniRestorer presents a strong universal image restoration framework that achieves impressive performance across single, mixed, OOD, and even real-world degradations. Its combination of multi-granularity degradation representations, hierarchical expert specialization, and uncertainty-aware routing enables both robustness and precision, outperforming prior all-in-one baselines and approaching single-task SOTA results.\nHowever, the core mechanism raises conceptual concerns: the training pipeline is overly complex, and the single-expert activation strategy appears suboptimal and parameter-inefficient. Clarification on these design choices—especially how the MoE routing behaves under mixed degradations—would be crucial to fully assess the method’s contribution.", "questions": "The second weakness is the main issue that confuses me the most. I would appreciate a clear explanation, and I may consider raising my rating if it is addressed convincingly.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper’s fine-grained degradation representation learning is a clear strength. By retraining a DA-CLIP–based extractor with fine-grained textual labels (e.g., light/medium/heavy noise or haze) and contrastive supervision, the authors enable the model to capture not only degradation types but also intensity levels.  Supplementary t-SNE visualizations results show that these representations are separable at both coarse and fine granularity, providing a solid foundation for the subsequent hierarchical clustering and multi-granularity expert design.\n2. Demonstrates strong cross-distribution generalization, maintaining stable PSNR/SSIM across single, mixed, OOD, and real-world degradations, with zero-shot gains on unseen types.\n3. Uncertainty-aware hierarchical routing adaptively selects coarse or fine experts for robustness or precision, reducing routing ambiguity and representation conflict while matching or surpassing single-task performance.\n4. Comprehensive evidence beyond parameter scaling: broad baselines and ablations show gains arise from division of labor and routing, with the LoRA variant retaining near full-model performance.", "weaknesses": "1. The inference scheme activates only a single expert at a time, which limits the expressive power of the MoE and introduces substantial parameter redundancy, as many experts remain unused for each input.\n2. When the MoE system encounters mixed degradations, how frequently does the router fall back to the 0-th level (coarse) expert?\nIf this fallback occurs in most cases, it is unclear why the model significantly outperforms a single Restormer trained directly on mixed degradations. Conversely, if the router instead activates a fine-level expert (e.g., a “rain” or “haze” expert) for a mixed input such as rain-haze, it would contradict the intended degradation clustering principle and could potentially degrade performance. Clarification on the router’s behavior and expert selection under mixed-degradation inputs is needed.\n3. The overall training pipeline is overly complex and resource-intensive. It requires first training a degradation extractor, then performing hierarchical clustering, followed by separate training for multiple experts and an additional router stage.\n4. The paper does not clarify how data sufficiency is ensured for the fine-granularity experts. Since the hierarchical clustering process recursively divides the training set into smaller subsets, some fine-level clusters may contain only a limited number of samples. It is unclear whether the authors applied any method to avoid underfitting or data imbalance across experts.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)