Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration
Authors: Guanglu Dong
Chunlei Li
Chao Ren
Jingliang Hu
Yilei Shi
Xiao Xiang Zhu
Lichao Mou
Venue: ICLR 2026 Poster (2026)
Keywords: multi-domain all-in-one image restoration
prompt learning
low-level computer vision
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recently, significant breakthroughs have been made in all-in-one image restoration (AiOIR), which can handle multiple restoration tasks with a single model. However, existing methods typically focus on a specific image domain, such as natural scene, medical imaging, or remote sensing. In this work, we aim to extend AiOIR to multiple domains and propose the first multi-domain all-in-one image restoration method, DATPRL-IR, based on our proposed Domain-Aware Task Prompt Representation L}earning. Specifically, we first construct a task prompt pool containing multiple task prompts, in which task-related knowledge is implicitly encoded. For each input image, the model adaptively selects the most relevant task prompts and composes them into an instance-level task representation via a prompt composition mechanism (PCM). Furthermore, to endow the model with domain awareness, we introduce another domain prompt pool and distill domain priors from multimodal large language models into the domain prompts. PCM is utilized to combine the adaptively selected domain prompts into a domain representation for each input image. Finally, the two representations are fused to form a domain-aware task prompt representation which can make full use of both specific and shared knowledge across tasks and domains to guide the subsequent restoration process. Extensive experiments demonstrate that our DATRL-IR significantly outperforms existing SOTA image restoration methods, while exhibiting strong generalization capabilities. Code is available at https://github.com/GuangluDong0728/DATPRL-IR.

Published as a conference paper at ICLR 2026
LEARNING DOMAIN-AWARE TASK PROMPT REPRE- SENTATIONS FOR MULTI-DOMAIN ALL-IN-ONE IM- AGE RESTORATION
Guanglu Dong!.*
Chunlei Li
Chao Ren
Jingliang Hu²
Yilei Shi
Xiao Xiang Zhu
Lichao Mou
Sichuan University
MedAl Technology (Wuxi) Co. Ltd.
Technical University of Munich
Gongguanglu@stu. scu, enu.en
lichao. htw.aoommouummddiiggigggag..co
ABSTRACT
Recently, significant breakthroughs have been made in all-in-one image restora- tion (AiOIR), which can handle multiple restoration tasks with single model. However, existing methods typically focus on a specific image domain, such as natural scene, medical imaging, or remote sensing. In this work, we aim to extend AiOIR to multiple domains and propose the first multi-domain all-in-one image restoration method, DATPRL-IR, based Prompt Representation Learning. Specifically, we first construct a task prompt pool containing multiple task prompts, in which task-related knowledge is im- plicitly encoded. For each input image, the model adaptively selects the most relevant task prompts and composes them into an instance-level task represen- tation via a prompt composition mechanism (PCM). Furthermore, to endow the model with domain awareness, we introduce another domain prompt pool and distill domain priors from multimodal large language models into the domain prompts. PCM is utilized to combine the adaptively selected domain prompts into a domain representation for each input image. Finally, the two represen- tations are fused to form a fomain-aware task prompt representation which can make full use of both specific and shared knowledge across tasks and domains to guide the subsequent restoration process. Extensive experiments demonstrate that our DATPRL-IR significantly outperforms existing SOTA image restoration methods, while exhibiting strong generalization capabilities. Code is available at https://gite www.commc//aoo/o http:///..ooommeeaaaaaaaaaaaaaee http://hhhho..cmmmm//oo//oooon// /2TT////R---D
proposed Domain-Aware Task
INTRODUCTION
Image restoration (Guo et al. 2024 Chen et al. 2022 Zamir et al.) 2022 Liang et al. 2021 Zamir al. 202T) has long been a fundamental research computer vision, aiming recover high-quality images from their degraded versions. With the advancement of deep learning, image restoration has found widespread applications across multiple image domains, including natural scene, medical imaging, and remote sensing. Early explorations mainly focused designing independent models for different tasks within each domain, such as natural image super-resolution (SR) (Dong et al.
2014), natural image deraining (Dong al. 2025b), natural image deblurring (Chakrabarti 2016),
CT demoising (Chen et al.] (017). MRI SR (Chen et al. 2018). PET synthesis (Luo et al. 2022
remote sensing image (RSI) SR (Lei al. 2017), RSI cloud removal (Liu et al.) (025). RSI dehaz-
ing (Shen et al. 2020), etc.. However, training separate models is undoubtedly time-consuming and resource-intensive, and this greatly limits their applicability in complex real-world scenarios.
To address the above challenge, all-in-one oll-in-one image restoration (AiOIR) (Li et al. 2022 Cui et al.
2024 Potlapalli et al., 2023) Zamfir et al. 20255] Conde al. 2024 Zhang al. 2025) has gained
increasing attention in recent years, as it seeks to provide unified solution for handling multiple
Work done during an internship at MedAl Technology (Wuxi) Co. Ltd.
1 "Corrsponniig author.

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Published as a conference paper at ICLR 2026
SR
Average PONR
Denining
Dehzzing
PET Symteets
Cloud Farroval
Prompitt (Meath
The aweihhr (DVM10002))
RBi
Denaising
MRESR
PET Syrthesis
Peraining
Figure 1: This paper makes a preliminary exploration of (MD-AiOIR), aiming at further extending the restoration capability of single model to a broader range of tasks and image domains, including natural scene, medical imaging, and remote sensing.
all-in all-in-one image restoration
restoration tasks with single model. AiOIR first emerge in natural scene, by leveraging implicit or Conde et al.) 2024), contrastive learning (Li et al. 2022).
explicit prompts (Potlapalli et al., 2023
degradation classification (Hu et al. 2005 prior information (He et al. 2024), or mixture-of-expert
(MoE) architecture (Zamfir et al. 2025) enable the restoration networks to better distinguish
between different tasks. Meanwhile, inspired by the progress in natural scene, AiOIR methods have also gained popularity in medical imaging (Chen et al. 2025 Yang et al. 2025b 2024a).
Though existing methods have achieved remarkable success, they primarily focus on maximizing task differences within single domain and often overlook the shared commonalities across tasks. They also do not consider the differences or connections between different image domains. When faced with more restoration tasks and image domains, they will face increased learning difficulty.
In this work, we make the first exploration of multi-domain all-in-one image restoration (MD- AiOIR), aiming unify diverse restoration tasks across multiple domains within a single model. Inspired by the concept of prompt pool in L2P (Wang et al. (022c),, we propose domain-aware task prompt representation learning (DATPRL), which adopts dual- puol-prompt--ool design to learn prompt representations that carry both task-relevant and domain-relevant knowledge. Based on DATPRL, we introduce the first MD-AiOIR method, DATPRL-IR. Specifically, we first construct task prompt pool with numerous task prompts. For each input image, our DATPRL-IR can adap- tively select the most relevant task prompts through a similarity based query mechanism. To express more diverse instance-level information, combine the selected task prompts into a task prompt representation. The task prompts are opti- mized jointly with the restoration objectives, ensuring the learning of task-specific knowledge while allowing knowledge sharing across tasks. Additionally, to endow the model with domain aware- ness, we build separate domain prompt pool to store domain-related knowledge. We leverage the powerful image understanding ability of multimodal large language models (MLLMs) and employ a cross-modal alignment distill domain priors from MLLMS (e.g., LLaVA (Liu al., 2024) into the domain prompts. Similarly, our DATPRL-IR will adaptively select the most relevant domain prompts for each input image, and then apply PCM to combine them into an instance-level domain prompt representation. The two prompt representations are then fused into the final domain-aware task representation to guide the subsequent restoration process. Our method effectively exploits the shared knowledge across different tasks and domains, significantly reducing the learning difficulty and facilitating performance improvement across tasks. As illustrated in Figure [ under the guid- ance of domain-aware task prompt representations, our DATPRL-IR methods, demonstrating strong generalization capability.
propose prompt composition mechanism (PCM) to
signniiiaan significantly surpasses existing
Our main contributions can be summarized follows: (1) To the best of our knowledge, propose all-in-one image restoration method, DATPRL-IR, which can handle diverse restoration tasks across multiple domains. (2) Through the proposed domain-aware task prompt
the first
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Published as a conference paper at ICLR 2026
representation learning, our DATPRL-IR effectively leverages both specific and shared knowledge across tasks and domains to guide the restoration process. (3) Extensive experiments demonstrate that our method outperforms existing SOTA image restoration approaches on MD-AiOIR tasks.
2 RELATED WORK
Single-Task Image Restoration. With the development of deep learning (LeCun et al. 2015).
image restoration techniques have made continuous progress across multiple imaging domains,
cluding natural scene (Dong et al. [2014] Zhang et al. 2017 Dong et al. 2025b] Li et al. 2025b).
medical imaging (Chen et al. 2018 2017] Luo et al. 2022), and remote sensing (Lei et al. 2017
Luo
Wu et al.] [023] Liu et J 2025). By leveraging specific designs for different domains and tasks, wide variety restoration sub-tasks have flourished. Recently, with the growing demand for multi- task image restoration and the continuous evolution of foundation backbones (e.g.. CNNs (He et al.,
2016 RRnneberger et al. 2013), Transformers (Vaswani et al., 2017 Dosovitskiy et al. 2020), and
Mamba (Gu & Dao, 2023 Zhu al. 2024 ). series of general image restoration baselines (Zamir
ct al.
2021] Liang et al. [022]] Zamir et al. 2022: Wang et al., 2022a Chen et al. 2022 Guo et al,,
2024
Li al. (2225a) have also emerged, which capable of handling diverse types of degrada-
tions within a unified model architecture. However, these methods require training a separate model
for each individual task, which is time-consuming and labor-intensive.
All-in-One Image Restoration. To overcome the limitations above, various all-in-one image restoration (AiOIR) frameworks (Li al. 2022 Cui et al.. 2024 Conde et al. 2024 Potlapalli
et al. 2023 Zamfir et al.) 2025) have continuously emerged and achieved sustained breakthroughs, especially in the natural image domain. AirNet (Li et al. 2022) the first to achieve AiOIR
through contrastive learning (Chen et al. 2020 He et al. (020).. IDR (Zhang et al., (023) integrates ietradation-speciife priors into the restoration process enhance performance. PromptIR (Potla- palli et al. 2023) uses learnable prompt components to encode different degradation information. DA-CLIP Luo et al.) 2023) decouples degradation and content semantics based on CLIP (Rad-) 2021), making the model more sensitive to various degradation knowledge. Instruc- tR (Conde et al.] 2024) guides the image restoration model through human- instructions. MoCE-IR (Zamfir al. 2025) introduces complexity experts within mixture-of-expeer
ford et al.
(MoE) architecture to efficiently allocate task-specific resources. DCPT (Hu et al. 2025) propose a degra- dation classification pre-training strategy to classify the degradation type of input images. In addition to the methods for natural domain, recent AiOIR techniques have also started to gain attention in the
field of medical imaging (Yang et al.) [224a] 2025b Chen et al. (025). However, current research mainly focuses on exploring a specific domain, with most approaches aiming to better distinguish between different tasks while overlooking the commonalities between them.
Prompt Learning-basee based Image Restoration. Inspired by the success of prompt learning in natu- ral language processing (Shin al., [020)] Brown al.] 2020), high-level computer vision (Wang et al. 2022c|b), (b), and multi-modal models (Zhou et al., 2022 Yao et al.] (023). it has also been widely applied in image restoration recently. PromptRestorer (Wang et al. (023a) takes the ad-
vantage of prompt learning to perceive degradation, achieving progress on individual tasks such as image deraining, deblurring, and dehazing. SFD (Dong et al. 2025a) trains learnable antonymous prompt pairs in an adversarial manner to promote global discrimination for super-resolution images. PromptIR (Potlapalli et al. 2023) is the first to explore the capability of prompt learning in all-in-one image restoration, and it subsequently inspires series of prompt- based all-in-one restoration meth- ods (Gao et al. 20224] Kong et al., 2024; Wu et al. 2025 Ma et al. [023 Ai et al. 2024 Conde et al.) 2024), which employ explicit implicit prompts
guide restoration process. In this work, differ- ent from existing methods, we propose a novel Nomain-awan task prompt representation learning method, which effectively leverages both both the specific and shared knowledge across restoration tasks and image domains to guide multi-domain all-in-one image restoration.
DATPRL-IR FOR MD-AIOIR
3.1 DOMAIN-AWARE TASK PROMPT REPRESENTATION LEARNING
Motivation. In this work, we aim to take the first step towards Multi-Doman Domain All-in-One Image Restoration (MD-AiOIR), extending AiOIR to more restoration tasks across multiple image do-
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"The image bird's-eye view mony buildings in city with relotively low brightness......"
Domain-Aware Task Prompt Representation
PCM
Learning
Fiiid Projector
"The image disploys a colorful and healthy salad vegetables......
Domain Prompt Pool
Task Prompt Pool
K
variety of
"The image is black and white photo with up view brain....."
Encoder
Coršim between Q
TOP
LLaVa-1.5-7B
Cross-Madal Alggmmet
PCM
PCM
PR
PR
HQs
CosSim: (o.s, 0.2, 0.1, 0.05, 0.05]
pR
AGF
Freezing
Training
AGF
Adapáive
Fusion
Input Images
Output Images
CA
Cress Attencion
Multi-Domain All-in-One Image Restoration for Natural, Medical, and Remote Sensing Images
all-in all-in-one image restoration. By introducing domain-aware task representation learning, DATPRL-IR can fully utilize both spe- cific and shared knowledge across tasks and domains, effectively reducing the learning difficulty of the model and improving its performance.
Figure 2:
Framework of the proposed DATPRL-IR for
mains. A key challenge is how to alleviate the learning difficulties introduced by the increasing number of restoration tasks and image domains. Prior studies (Chen al. 2024 Zhang et al. 2023) have found that different image restoration tasks share certain inherent commonalities or similar latent representations and there is certain mutual promotion effect between different restoration tasks (Conde et al., 2024), such as super-resolution and motion deblurring. Additionally, though images from different domains exhibit their own distinet visual characteristies, they share some common terms. Combining these specific and shared visual characteristics can facilitate the dis- crimination of the image domain. For instance, "grayscale human organs" typically corresponds to medical images, while "bird's-eye view + buildings" often indicates remote sensing scenarios. Therefore, we infer that effectively leveraging both the specific and shared knowledge across tasks and domains can help reduce the learning difficulty and further enhance the restoration performance. Inspired by L2P (Wang et al.) 2022c), prompt pools offer an effective way to encode and organize both specific and shared knowledge. Building on this insight, we propose Domain-Awart Task Prompt Representation Learning and introduce the first MD-AiOIR method, DATPRL-IR.
Overall Framework. As illustrated in Figure 2 our DATPRL-IR mainly consists of an en- cocer-decoder based restoration backbone, a task prompt pool, a domain prompt pool, a CLIP (Rad-] Ford et al. 202 l) text encoder, and the LLaVA-1.5-7B (Liu et al. 2024) model. The task and domain prompt pools store N₂ and NJ prompts, respectively. These prompts implicitly encapsulate knowl- edge related to restoration tasks and image domains. Given degraded input image, our model adopts query-retrieval-compssii
paradigm to adaptively query both prompt pools to retrieve the most relevant prompts for the task and domain, which are then composed into two representa- tions: task prompt representation PR, and a domain prompt representation PRa. Subsequently, these two representations are integrated through a aross-attention mechanism to produce a domain- aware task prompt representation PR., which can effectively guide the restoration process.
Task Prompt Pool. Task prompt (TP) pool is used implicitly store both specific and shared knowledge across different tasks, and each prompt in TP pool is represented as a pair of a key Klask d and value Vlask E 7'x We use learnable projector map the middle feature Fmid of the input image extracted by the encoder of the restoration network into a query Quakk e I2d with the same dimension as Kusk Based on the cosine similarities g between the Qiask and each K the top k most relevant values Vinck can be retrieved from the TP pool. To enable the limited set of prompts to provide more diverse instance-level prompt guidance, design prompt composition
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boak
tour 1sts
coastal sysppe
bir dseve
TUM
coastal
buig
abderina
brann
Plaa
Canoby 1
human body
Aunn persbral
red seat
City
Whit Whitee
fulorful
ront' Worl Cloud of DF2K
black
WIVid ba biueltei
BEAghtness
TOCURATIE
Corebellie
Word Cloud of CoPro
Wond Cloud of CURK
Wond Cland AfPM
Word Cloud IXI MRI
T-SNE of featunos
Figure 3: A partial visualization of the word clouds generated from the text descriptions produced by LLAVA, and the t-SNE clustering analysis of the text descriptions corresponding to the 9 datasets from different domains and tasks. can be observed that images from different domains exhibit their own characteristics while also sharing certain overlapping features.
mechanism (PCM) to combine the selected Vink into an instance-level task prompt representation PR, according to the similarity scores skak
exp(s)akk (Tun))
PR, Σ
yisk
Σ exp(s / EEk
jEK
where ja denotes the relative cosine similarity among the selected prompts, and Task is the tem- perature parameter. During training, task prompts are optimized jointly with restoration objectives, ensuring the learning of task-related knowledge while allowing knowledge sharing across tasks.
Domain Prompt Pool. As shown in Figure 3 images from different domains exhibit their own characteristics while also sharing certain overlapping features. Integrating these characteristics will help the model to better determine the domain of input image and to enrich its domain knowledge. To this end, we construct another domain prompt (DP) pool to store and organize such domain priors. The domain prompts in the DP Pool are also constructed image, we use another projector to map the shallow features extracted from the first layer of the restoration backbone into a domain query Qdom Based the cosine similarity between Qdem and the key Kdom of each domain prompt, we select the top k most relevant prompts. Similar to the TP
set of key-value pairs. For each input
pool, also employ the PCM to combine the value Vilum of these candidates into instance-level domain prompt representation PR To endow PR, with rich and interpretable domain knowledge, employ LLaVA-1.5-7B to generate multi-perspectivy descriptions for the high-quality images (HQs) corresponding to each input, covering aspects such as image content, color richness, object category, brightness, and camera/viewpoinn During training, these textual descriptions fed into the CLIP text encoder to obtain the corresponding text features F Frex. and the training of the DP Pool constrained by the following cross-modal alignment loss:
1 (1 cos (P Fiest B
Calign
(2)
n=1
where B denotes the batch size. During the joint training of our model, Lalign encourages the domain prompts to capture both specific and share domain knowledge that benefits the restoration objective, providing domain-related information to the network. It is worth noting that the LLLVVA and CLII will not be used during the inference stage without introducing any additional inference overhead.
Domain-Aware Task Prompt Representation. Finally, the task and domain prompt representations PR, and PR, will be fused through a cross-attention layer to learn a domain-aware task prompt representation dR which will be used to guide the restoration process. Considering that different layers of the restoration backbone may have varying demands for prompt information, inspired by UniECS (Liang et al., 2005),, we dynamically control the contribution ratio between backbone features and PR,, at each layer through adaptive gated fusion (AGF):
F = CrossAttn(ay, (q Fi, (1 a4) PRa)
(3)
where FF and F, denote the enhanced feature map and the pre-fusion feature map at the I-th layer respectively, and a; [0, is a learnable gating coefficient for the I-th layer. AGF allows each layer to independently learn the optimal fusion ratio, enabling adaptive integration of PR and F,.
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3.2 PROMPT POOL REGULARIZATION
We introduce series of regularization terms to avoid the model degenerating into undesirable be- haviors: e.g., over-relying small subset of prompts, learning redundant or highly correlated prompt contents, especially during the early training phase. Firstly, we adopt a diversity regulariza- tion encourage diversity of the learned prompts. Given a prompt pool with N prompts, we first compute the pairwise cosine similarity matrix of their values V E IR. fxd
V VT
Sú IV IV
(4)
where Sty is the pairwise cosine similarity. To exclude self self--imilaarity we apply mask M I- where IN is the N x N identity matrix. The diversity regularization loss is then formulated as:
Caiv N(N 1)
Σ
My max (0, Tdiv).
where Tdiv is a predefined similarity threshold. Minimizing Caiv encourages prompts to occupy distinct regions in the representation space, avoiding collapse similar contents.
Furthermore, we adopt a prompt entropy regularization to encourage more balanced utilization across the prompts. Given query keys [ we first compute the selection probability p, of each prompt based on the cosine similarity score 4j between and kj- The selection probabilities of each prompt obtained via softmax and their entropy is computed as:
Fd mapped from the input image and pool of P prompt
exp(s;)
Pj
H(p) =
Σ Pg logPj. Pj+
(6)
Σ m=m exp(sm))
where (p) denotes the entropy of the probability distribution. Then the balance loss is defined as:
Lbal log H(p).
(7)
Lbal encourages balanced prompt utilization during training. In addition, to enhance the sensitivity of instance-level prompt selection, we apply contrastive regularization Ccon as detailed in the Appendix C All regularization terms are applied both the two prompt pools.
3.3 OVERALL OPTIMIZATION OBJECTIVE
The final training objective combines the primary reconstruction loss with cross-modal alignment loss and prompt regularization terms, and the total loss can be formulated as follows:
C Apinhhpiz + A + Datignhatiig + Adivhddiv + Abal Lbat Reonoron
Reconstmaction I Loss
Cross Modal Alignment and Prompt Regularization
where Epic and Lift are & loss in the RGB and Fourier domain respectively and Apizs X Antigns Aasus Abats and Acon hyperparameters controlling the relative importance of each loss component.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
To demonstrate the effectiveness of our method, we conduct experiments mainly from the 2 aspects: (1) 6-task and 3-domain experiment, (2) 9-task and 3-domain experiment. We consider 3 image do- natural, medical, and remote sensing images-wiin tasks from each domain. For the 6-task setting, we include 2 tasks per domain: natural image 4x super-resolution (SR) and deraining, medical MRI SR and CT denoising, and remote sensing im- age 4x SR and cloud removal. For the 9-task setting, we introduce one additional task per domain: natural image motion deblurring, medical PET synthesis, and remote sensing image dehazing.
diverse selection of image restoration

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison between our method and other SOTA methods on 3 domains & tasks experimental setting. The best and second-best best metrics are highlighted in bold and underline.
Domain
Nutural Image
Medical Image
Remote Senoing Image
Average Performance
Task Dataret
Super Resolution
DIV DIV2K-VAL
Deminiig R0in
MRI SR
TXI MRT
CT Dencising AAPM-Magg
UCMerced
RS
Clond Removal
CUNK CRI
Medland
Yetr
PENK
SSIM 7 PSNR
SSIM
PSNR
SSIM
PSNR 7 SSIM
PSNR 1 SSIM 7 PSNR
SSIM 1 PSNR PPSNR
SSIM
SingloTTak Methed
MPHNN
CNP92021
28,82
0,8155
38,07
0.9817
26.84
0.8891
33.601
0.9259
27.70
0.7750
25:35
0.7389
30.06
0,8534
SwinlR
RCCVW2021
28.61
0
36,07
0.9736
26.06
0.8766
0.9243
27.29
0.7565
24.36
0.6552
29.32
0,8482
CNPR2022
0.8158
36. 54
0,9822
0.9017
09268
25.01
0.7844
25.06
0.7541
30.42
0.8608
NATNet
DCCV2022
28.73
0.8146
37.05
0,9773
27.32
0.8950
0.9270
27.87
0.7808
25.99
30:11
0,8595
All-inOOe Matood
Transwzshher
UVPR2202
27.40
0.7643
53.20
0.9495
24.59
0.8181
31.98
0.5040
25.97
116953
22.95
0.5732
27.65
0.7K33
PromedRR
NeurIPS2023
28.77
0.8160
18.71
0.9811
27.61
0.9023
33.71
0.220
28.05
0.7880
25.81
0.7818
10.44
0.8610
AMIR
MICT
28.78
0.8139
55.10
0.9830
26. 90
0,8791
13.66
0.9322
27.87
0.7797
25.75
0.7474
50.05
0.8548
DEFIR
CVPR2025
27.69
0.7845
37.33
0.9745
24.59
0.8181
32.66
0.9137
25.97
0.6933
26.02
0.3072
29:04
0.8152
AdaIR
RCLR2025
26.81
0.8157
38.19
0.9816
27.54
0.9039
33.68
0.9256
27.99
0.7840
26.03
0.7575
30.37
0,8611
MoCEIR
CVPR2225
26.16
0.8156
38.64
0.95400
27:75
0.9122
35.72
0.9278
26.00
0.7843
26.06
0.7615
30.40
0.8627
Mun Domain All in One Method
DATPRL IR. 61
(Ous)
28,95
0.8191
39.56
0.9965
27.88
0.9053
0.9778
29.29
0.7917
26.12
0.7612
.0.77
0.8653
DATPRL IR- 7T
(Ouns
25.03
0.8183
39.65
0.9866
27.76
0.903
33.76
0.209
26 38
0.7908
25.91
0,7594
30.74
0.8643
DATPRL JR.
(Ouns)
39.64
0.9047
09200
0.7920
25.92
0.7590
30.74
0.8647
DATPAL 1R
(Ours
29.05
0,8181
39.67
0.9867
27.86
0.945
09273
28.31
0.7913
26.00
0.7592
30.78
0,8645
Input
Swin!
AdalR
MOCHIR
DATPRE-IR (Ours)
GT
Figure 4: Comparison of our DATPRL-IR with other SOTA methods on 6-task and 3-domain setting.
Datasets and Evaluation Metrics. The training datasets for each task are as follows: Natural im-
age SR is trained on DF2K (Agustsson & Timofte 2017 Timofte et al.. (017) dataset (DIV2K
Flickr2K) with 4x bicubic downsampling. Natural image deraining is trained using Rain100L. (Yang
ctt al. 2019). Natural image deblurring uses the GoPro (Nah et al. (017) dataset. Following Yang
itt al. (2024a]b), medical MRI SR is trained on the IXI MRI dataset. Medical CT denoising uses
dataset from the 2016 NIH AAPM-Mayo Clinic Low-Dose CT Grand Challenge (McCollough
et al., (017)) Medical PET synthesis is trained on the PolarStar m660 dataset, where both low-
quality (LQ) and high-quality (HQ) PET images are reconstructed via the standard OSEM (Hudson & Larkin 1994) method. Remote sensing image SR is trained on the UCMerced Land Use (Yang & Newsam 2010) dataset with 4x bicubic downsampling. Remote sensing cloud removal is trained us- ing CUHK CR1 (Sui et al. 2024) dataset. Remote sensing dehazing is trained on RICEI (Lin al.) 2019). which provides hazy and clean image pairs. Data augmentation including random cropping, horizontal flipping, and rotation applied to improve robustness. Evaluation is performed on the corresponding test sets, using PSNR and SSIM (Wang et al. 2004) in RGB space as the primary metrics.
Implementation Details. We train our model under PyTorch (Paszke et al. 2019) framework using the Adam (Kingmal 2014) optimizer with 31 0.9, _3 0.99. The learning rate is initialized at 4 x 10-1 with cosine annealing. Batch size is set to 12, and we train for 1000K iterations on NVIDIA RTX 5090 GPUs We set the diversity threshold Taiv 0.1. The loss weights are set to Aatign = 1.0. Amv 0.1, Au = 0.1, and Apal = 0.1. For each prompt, the key is defined as a 1x1024 vector, while the value is set to 2x1024. The numbers of prompts in both the task and domain prompt pools are set to 15, with top-k selection configured as k=3 for the task prompt pool and k=5 for the domain prompt pool. The projector is a 3-layer lightweight CNN (mainly including and MLP), and the dimensionality of its output is 1024. To ensure a fair comparison, all competing methods are trained using the loss functions and specific training strategies adopted in their original papers, while all other training setting are kept the same those used in training our model. For more description on datasets and implementation details, please refer to Appendix
Conv2d, AdaptiveAvgPool22.
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(Ours) Figure 5: Comparison of our DATPRL-IR with other SOTA methods on 9-task and 3-domain setting.
Input
SwinIR
NAFNet
PromptIR
4.2 MUL.TI-DOMIIN ALI.-IN-ONE IMAGE RESTORATION
Results on 6-task and 3-domain all-in-one image restoration. validate the effectiveness of our approach, we compare with several SOTA AiOIR methods (Zamfir et al. [0255] Cui ct al.) [024] Tian et al. 2025 Yang et al. 2024a) Potlapalli et al. 2023 Valanarasu et al. 2022) and classic image restoration baselines (Chen et al. 20.22 Zamir et al.) 2022] Liang et al. 2021 Zamir et al. 2021]. As shown Table our DATPRL-IR achieves almost comprehensive superionity across all six tasks, with an average PSNR improvement of 0.37 dB over the SOTA MoCEIR, and nearly 1 dB gain on the natural image deraining task. Furthermore, as illustrated in Figure 4 our method is able more thoroughly remove degradations and reconstruct clearer image details compared other methods. These results convincingly demonstrate the effectiveness of our proposed domain-aware task prompt representation learning in guiding image restoration.
Results on 9-task and 3-domain all-in-one image restoration. To further evaluate scalability of our DATPRL-IR, we sequentially add three tasks-natural image deblurring, PET synthesis, and remote sensing image dehazing to train our 9-task (9T) model, while also obtaining intermediate 7- task (7T) and 8-task (8T) models. As shown in Table it can be clearly observed that when the task number grows from 6 to 9, our method does not exhibit significant performance degradation on the original tasks, even achieves certain degree performance improvement. This provides strong evidence for our claim that different tasks indeed share transferable knowledge that complement each other, and our method can effectively exploit both the shared and specific knowledge to enhance model robustness when facing larger number of tasks. As illustrated in Figure 5 images restored by our method exhibit clearer textures and fewer artifacts.
Due to the limited space, additional quantitative and qualitative results, zero-shot and gener- alization results and analysis are provided in Appendix D
4.3 ABLATION STUDIES
The effects of task prompt pool and domain prompt pool. Table 2 investigates the impact of task prompt (TP) and domain prompt (DP) pools under 6-task and 3-domain experiment setting, Using any one type of prompt pool can provide clear improvements over the baseline, indicating that both task-aware and domain-aware representations contribute useful prior knowledge. Impor- tantly, combining both the two pools leads to the best performance, the PSNR on the three datasets respectively increased by 1. 1.22dB, 0.10dB, and 0.27dB across the three tasks. The above abla-
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tion results highlights the effectiveness of our dual -rommpt design in enhancing generalization and robustness for scenarios with diverse tasks and domains.
Table 2: Effect of task prompt (TP) pool and domain prompt Table 3: Effect of prompt numbers and top-k selection.
(DP) pool on DATPRL-IR.
Deraining on Rain10d1.
Denoising on AAPM
RSI on UCMerced
TP Pool
DP Pool
6-Task Aver.
TP Pool
DP Pool
Perfoemance
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Nums
Top
Noms
Top
PSNR
SSIM
X
x
38.34
0.9823
0.9269
26.02
0.7844
10
10
3
1
10
10
5
30.64
30.53
0.8612
0.8907
X
39.32
D.9855
33.76
0,9282
28.16
0.7901
15
1
15
30.45
0.8007
38.88
0.9850
33.74
0.9268
28.12
0.7897
15
15
30.77
0.8653
30
30
30.73
0 0.865
39.56
0.9865
33,80
0.9278
28.29
0.7917
20
30,70
0.8638
The effects of prompt numbers and top-k selection. As shown in Table we present the average performance over six tasks under different configurations of prompt numbers and top-k selection. The results reveal two trends. First, enlarging the prompt pool to moderate size improves perfor- mance by offering richer choices, while excessively large pools lead to diminishing or even negative returns, redundant prompts may dilute useful signals. Second, for top-k selection, using too few prompts limits expressiveness, while selecting too many reduces specificity. A balanced config- uration not only preserves specificity but also better leverages the shared knowledge across tasks and domains. Óverall, these results indicate that a moderate prompt capacity with carefully chosen retrieval breadth are key achieving robust and generalized image restoration performance.
The effects of different MLLMs. To explore our method's 's sensitivity to the choice of MLLMs. We test our model by replacing LLa (Liu et al. (024) with two different MLLMs, LLaVA1.5- 13B (Liu et al. (2024) and -eeen---L-2---- Table|4] the performance of our method remained stable under MLLM models of different parameter scales, with only marginal differences. demonstrating our method is not strongly dependent on a specific MILM. This is because we only rely on relatively coarse domain -lveel semantics (e.g., "main image content", "shooting view", "brightness", "color" etc.), and any MLLM capable of providing such descriptions is sufficient.
(Yang et al., 2025a Bai al. 2025). As shown in
The effects of different prompt designs. To explore the effectiveness on different prompt designs, we replace our domain prompt pool with one fixed explicit text prompt per domain (e.g.. "This is an MRI image". "This is natural image") and simpler domain encodings (one learnable 2 1024 tensor per domain). As shown in Table both alternatives underperform our domain prompt pool on almost all tasks. Our method does not rely on single fixed prompt per domain, but instead implements "shared + specific" prior modeling and learns a set of diverse domain-aware priors, allowing instance-level adaptive selection rather than forcing all MRI (or CT RSI/ / Natural) images to share one identical prompt. Furthermore, using explicit prompts would require the user to know the domain beforehand, which contradicts blind-restoration setting.
Table 4: Effectiveness on different MLLMs on domains & 9 tasks experimental setting. The best metrics are highlighted in bold.
Natural SR
Deruining Rain
CT Denoising
PET Synthesis on PolarStar
RSI SR
RSI Dehazing on RICEI
Task & Dataset
DIV2K-Val Val
AAPM
U'Mereed
Method
PSNR 1 SSIM
PSNR
SSIM 1 PSNR
SSIM f PSNR 1 SSIM
PSNR
SSIM f
PSNR 1 SSIM
LL13AL55-13B
29.04
0.8180
39.71
0.9868
33.77
0.9272
37.08
0,9500
28.31
0.7918
26.92
0.9347
LLaVALS-7B
29.05
088181
39.67
0.9867
33.77
0.9273
37.12
0.9502
28.31
0.7913
26.94
0.9347
29.07
0.8182
39.69
0.9868
33.77
0.9230
37.11
0.9501
28.31
0.7914
26.82
0.9351
Table 5: Effectiveness on different prompt designs on domains & 9 tasks experimental setting. The best metrics are highlighted in bold.
Tusk & Dutaset
Demining on Rain
Deblurring GolPio
MRI SR
PET Nynthesis PolarStar
Cland Remonal on CUHK CRI
RSI Dehazing
IXI MRI
RICEL
Method
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Explicit Domain Prompts
39.59
0.9865
29.31
23.77
0.9035
37.08
0.900
25.76
0,7573
25.91
0.9296
Simple Domain Encodings
39.52
0.9863
29.21
0.8814
27.70
0.9031
37.02
0.9496
25.81
0.7566
25.81
0.9213
Demain Prompt Pool (Ous))
39.67
0.9867
29.57
0.8881
27.86
0.9045
37.12
0.9502
26.00
0.7592
26.94
0.9347

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Published as a conference paper at ICLR 2026
DIVIK Val
R000
DXT MRI eE00tto
AAPM
UCMrreed NS5
CUNK
Donoia Pork
Ttak
(a) Prompt selection distribution of domain prompt pool and task prompt pool on partial test sets.
Task Pool
Domain Pool
DIV2K Val
(b) Diversity of the prompts in the 2 prompt pool and instance-level (c) Contribution ratio of prompt repre- prompt representations on DIV2K-Val.
sentations and backhone features.
Figure 6: In-depth analysis of our method. Zoom in for better visualization.
4.4 IN-DEPTH ANALYSIS AND DISCUSSION.
Prompt selection distribution. As shown in Figure 6a| we visualize the selection distribution of prompts from both the domain and task prompt pools across six datasets. For the domain prompt pool, different datasets exhibit distinct distributions. Notably, medical datasets present highly uni- form selections due to their relatively homogeneous image content and color patterns. In contrast, the task prompt pool shows larger overlaps across datasets, suggesting that a considerable number of task prompts are shared. This observation confirms that our method can effectively leverage shared knowledge across tasks to enhance network performance.
Prompt diversity. Figure 6b presents pairwise similarity heatmaps of prompt values in the two prompt pools. It is evident that each prompt has learned distinct content with little redundancy, thereby providing the model with diverse options. Furthermore, to examine instance-level behav- ior, we visualize the similarities between final prompt representations generated for different input images from the DIV2K validation For the same task, our method produces prompt representa- tions with similar overall directions, while retaining instance-specific variations, indicating that our method enhances the instance-level diversity of learned prompt representations.
The contribution ratio of prompt representations and backbone features. We further analyze the learnable gating coefficient oj E [0, 1] between prompt representations and backbone features at each block from the middle layers to the decoder, which is mentioned in Sec. 3.1 As shown in Figure 6c., most blocks exhibit dominant reliance on backbone features, indicating that the network is still primarily driven by the restoration backbone while prompt representations serve as auxiliary guidance. Furthermore, earlier blocks at each scale rely more heavily on prompt representations compared to deeper blocks, and the large variations in contribution ratios across different layers further highlight the importance of adopting adaptive fusion ratios.
5 CONCLUSION
In this work, we proposed the first multi-domain all-in-one image restoration (MD-AiOIR) method, DATPRL-IR, which covers multiple restoration tasks across various image domains. By introducing domain-aware task representation learning, DATPRL-IR can fully utilize both specific and shared knowledge across tasks and domains, effectively reducing the learning difficulty the model and improving its performance. Extensive experiments show that DATPRL-IR outperforms existing SOTA methods and demonstrates excellent generalization abilities. We believe that this work lays the foundation for future research towards more unified restoration framework.
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ETHICS STATEMENT
The authors acknowledge that this work adheres to the ICLR Code of Ethics.
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APPENDIX
A USE LARGE LANGUAGE MODELS
Large language models were used solely for light editing tasks including grammar correction, spelling checks, and minor phrasing improvements to enhance clarity and concision.
B DISCUSSION ON BROADER IMPLICATION AND LIMITATION
In this paper, we propose a dual-prompt-pool approach tailored to MD-AiOIR. In partieular, analysis and experiments reveal that different lasks/domains share some common knowledge, while still preserving specific differences. Modeling these 'shared specific' priors can significantly re- duce the learning difficulty when facing more tasks and domains, and help improve restoration performance. We believe this insight opens a new perspective for moving image restoration toward more unified and scalable models across multiple domains. There are also several limitations in our framework. The training cost of our framework and single-task models. In addition, although our method demonstrates strong zero-shot behavior, extending it to a broader range of domains remains an interesting direction. It is worth further ex- ploring how to model the shared and different prior knowledge between various restoration tasks more efficiently and interpretably.
little bit higher than that of single-domain
C DETAILED EXPERIMENTAL SETTING
Datasets. The training datasets for each task are follows: Natural image SR is trained on the DF2K (Agustsson & Timofte 2017 Timofte et al.) 2017) dataset (DIV2K + Flickr2K) with 4x bicu- bic downsampling. Natural image deraining trained using Rain100L (Yang al.) 2019). Natural image deblurring uses the GoPro (Nah et al. 2017) dataset. Following Yang et al. (2024a b). medi- cal MRI SR is trained on the Ix MRI dataset. Medical CT denoising uses dataset from the 2016 NIH AAPM-Mayo Clinic Low-Dose CT Grand Challenge (McCollough et al. [017] Medical PET synthesis is trained on the PolarStar m660 dataset, where both low-quality (LQ) and high-quality (HQ) PET images reconstructed via the standard OSEM (Hudson & Larkin 1994) method. Re-
mote sensing image SR is trained on the UCMerced Land Use (Yang & Newsam 2010) dataset with
4x bicubic downsampling. Remote sensing cloud removal is trained using CUHK (Sui al., 2024) dataset. Remote sensing dehazing is trained on RICEI (Lin et al. 2019), which provides hazy and clean image pairs. Table presents the detailed numbers of training and testing images for each dataset. Data augmentation including random cropping, horizontal flipping, and rotation are applied improve robustness. We unify all inputs by converting every dataset sample into a standard 3- channel visual image before feeding it into the network. Natural and remote-sensing datasets are already stored as RGB images. For medical datasets that are not originally stored in RGB format, we first convert them into grayscale images, and then replicate the single channel three times to obtain a 3-channel representation. All images are stored in a visual format (PNG or JPG), ensuring consistent dimensionality.
Table 6: Detailed description of the datasets utilized.
Datasets
DF2K
Rain1001
GoPro
IXI MRI
AAPM-Mayo
PolarStur m660
UCMerced
Train
3450
200
2103
40500
18351
27837
1800
Test
100
100
1111
11400
211
2044
300
Tasks
Natural
Natural Deraining
Natural Debluring
MRI
CT Demoising
PET Synthesis
RST
Datasets
CUHK CRI
RICEI
Train
534
400
Test
134
100
Tasks
Cloud Removal
RSI Dehazing
Implementation Details. We train our model under PyTorch (Paszke et al. 2019) framework using
the Adam (Kingma 2014) optimizer with 31 0.9, 82 0.99. The learning rate is initialized
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Table 7: Examples of Lf LLaVA-generated textual descriptions for medical, remote sensing, and natural
images.
Medical Images
"The image is black and white photo of a brain, with close up view of the frontal Jobe." "The image is black white picture human body, specifically focusing on the abdominal area."
"The image is a grayscale medical image with low overall contrast, leaturing a bright background and a few small dark foci near the center."
Remote Sensing Images
"The image a large, green field with a white snowy ground and it is winter scene." river flows through it highway is visible nearby."
"The image bird's-eye eye view forest "The image is bird's eye view of large building with pool, surrounded by palm trees."
hrightness,
Natural Images
"The image features
red outfits walkinng through grassy field, one them currying basket."
"The image depicts a besutiful garden entrance featuring a red door with large vine growing up the side of the building."
"A colorful image taken from bost shows tourists on red seats under canopy looking over vivid blue sea toward detailed coastal cityscape."
at x 10-4 with cosine annealing. Batch size set 12, and we train for 1000K iterations on NVIDIA RTX 5090 GPUs We set the diversity threshold Täiv = 0.1. The loss weights are set to Aalign = 1.0, Adiv 0.1, Asm = 0.1, and Abal = 0.1. For each prompt, the key is defined as Ix1024 vector, while the value is set to 2x1024. The numbers of prompts in both the task and domain prompt pools are set to 15, with top-k selection configured as k=3 for the task prompt pool and k=5 for the domain prompt pool. The projector is a 3-layer lightweight CNN (mainly including Conv2d, AdaptiveAvgPooll2 LLa is primarily built from CLIP's ViT-L/14 visual encoder (Radford et al. (02T] and Vicuna-7B (Chiang et al. 2023), a language model based on the LLaMA (Touvron et al. [023) architecture. We instruct the -LaVA-vv.55-70 number of texts that exceed the CLIP text encoder length limit will be automatically truncated to 77 tokens. is worth noting that the LLaVA-v1.5-7B and the CLIP text encoder are only used during the training phase. During inference, neither the LLa VA-11.55577 nor CLIP is required, thereby introducing no additional inference overhead. In Table 7 we present several examples of LLa generated textual descriptions for medical, remote sensing, and natural images. These descriptions provide concise and discriminative domain cues that are sufficient for alignment.
and MLP). and the dimensionality of its output is 1024. The used
produce concise descriptions, and very small
Contrastive Regularization. In addition to the diversity regularization and prompt entropy regu- larization described in the main text, we also introduce a contrastive regularization to enhance the sensitivity of instance-level prompt selection. Specifically, the query with the keys of their most relevant prompts while pushing them away from the keys of unrelated prompts. The contrastive regularization is defined as:
adopt contrastive objective to align
exp((q, k+T)))
Lron =
log exp( k )/T + Σx- exp((q.k=))
where q denotes the query and (k+} } and {k-} } denote the sets of positive (selected) and negative (non-selected) prompt keys.
Compared Methods. We compare our DATPRL-IR with several SOTA AiOIR methods (Zamfir
et al.
2025 Cui et al. 2024 Tian et al. 2025 Yang et al. 2024a| Potlapalli et al., 2023 Valanarasu
eet al. 2022) and classic image restoration baselines (Chen al. 2022 Zamir et al., [022] Liang
al. 2021 Zamir et al.) (021). To ensure fair comparison, all compared methods are retrained from scratch on the same datasets as ours using their original loss functions and training strategies
as specified in their papers. All remaining training conditions were kept identical to ours, including the train/test splits, the
sampling strategy, data augmentations, and other shared
hyperparameters.
D MORE EXPERIMENT RESULTS
D.1 MULTI-DOMAIN ALL-IN-ONE IMAGE RESTORATION
We present detailed quantitative comparisons between our method and other approaches in Table g Our DATPRL-IR surpasses other SOTA methods across most tasks, demonstrating the superiority of our proposed domain-aware task prompt representation learning. Moreover, compared with the 6-task setting, it exhibits performance drop on the original six tasks, but even achieves further
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Table Quantitative comparison between our method and other SOTA methods on 3 domains & tasks experimental setting. The best and second-bes best metrics are highlighted in bold and underline.
Tank &
Darining us Ruin 1006.
Delarinn on GolPoo
MRI SR
CT Densiing us Mayo
PET Syathoes
RSI SR
Cloud Renaval
RS1 Detasing
Datanci
sn DDV22C--V
IXT MRI
un Polariis mu650
on UCMancel
un CLHH CRI
de RICE!
Method
MPRNe:
88:3270.8067
07.55/0.7997
2882//8857 (08570
26 70.8871
33,54/0.9255
36.72 70,9475
27.47 10 7676
25.20/0.7534
25 70.5625666
SwiilR
25.50-08109
37.55 0.9002
28.17/ 10
26. /0.3844
33.63 0.9051
36.78 10,9458
27.52 077757007570
25.39 00.7249
25 /0.9244
Restermer
76.81708150
15
37.30
10.451
16 /0.9245
NAPNe
28.64708128
209788
(18008
1566/0.9067
12.14/0.990
27.0020
25.06/0.790
16
All Oas Meilasd
Tramoraatter
2685 In
70:3616
16-1 (09440
70.9262
PromptiX
26.66708127
38.21
33.65
27.91
25.33
2636:
/U30013
25.87 10:7528
McCEIR
108152
36.2670.623
789278
0,0322
70,905.
Demrine Ane Maturl
(Ours)
2694/05347
6JR
BSS 4623
AJR
SBR
CARLTON
CARLTON
CARLTON
CARLTON
CARLTON TONTTON
CARLTON
Inpul
SwinIR
AdalR
MOCEIR
DAIPRL-IR (Ours)
Figure 7: Comparison of our DATPRL-IR with other SOTA methods on Natural Images.
improvements. We also provide additional qualitative visual comparisons in Figure 7 Figure and Figure 9 Obviously, compared other methods, method is able to remove degradations clearer and reconstruct more image details. These results significant demonstrate the effectiveness of our method.
D.2 ZERO-SHOT AND GENER ALIZATION PERFORMANCE
To demonstrate the zero-shot and generalization capability of our model, we add another three eval- uation settings: (I) Zero-shot unseen image domains; (2) Generalization unseen distributions; (3) Zero-shot on completely unseen restoration tasks. All experiments are performed using the well- trained model, and no fine-tuning is applied any method.
Zero-shot on unseen image domains. We test zero-shot super-resolution (SR) performance on two image domains never seen during training: Al-generated content (AIGC) images and comic images. Following (Zuo et al. 2025), we use the standard AIGC benchmark GenAl-Bench (Li et al. 2024)
for testing. Specifically, ll,, we randomly selected 100 images from each of the three subsets SDXL- Turbo, DeepFloyd, and Midjourney, and performed 4x bicubic downsampling to construct our AIGC SR test set. For comic image SR, we chose the most commonly used (arggal099MMaass et al. 2017)
as the test set. As shown in Table our method comprehensively outperforms other approaches in two unseen image domains, achieving PSNR improvements of 0.87dB and 0.45dB over the prompt- based PromptIR in Figure [0] demonstrates that our method can reconstruct much clearer texture details, with fewer blurry edges and artifacts.
SDXL- and Manga109, respectively. Furthermore, the visual comparison
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Input Figure 8: Comparison of our DATPRL-IR with other SOTA methods on Remote Sensing Images.
SwintR
NAFNet
Prompii
MOCEIR
(Ours)
Input
SwinIR
NAFNet
PromptIR
MOCEIR
DATPRL-IR (Ours)
Figure 9: Comparison of our DATPRL-II with other SOTA methods on Medical Images.
Zero-shot on completely unseen restoration tasks. We further test zero-shot performance on tasks not included in training: natural image desnowing. and specular highlight removal. For image desnowing, following (Lin al., 2025). we randomly select 2,000 pairs from the Snow 100K0 (Liu et al. (018)) test subset for evaluation. For specular highlight removal, we evaluate all methods on the large-scale real-world benchmark SHIQ (Fu et al.) 2021), which contains 1000 real image pairs for testing, As shown in Table 10, our method surpasses the SOTA MoCEIR by 0.92 dB and 0.55 dB PSNR on specular highlight removal and image desnowing tasks respectively, without any fine-tuning. At the same time, Figure II and Figure 12 show the comparison of the visual results of our method with other SOTA methods on these two completely unseen tasks. Our approach demonstrates extremely strong zero-shot capability, especially in specular highlight removal, where
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Table 9: Quantitative zero-shot super-resolution comparison between our method and other SOTA methods on AIGC and comic images. The best metrics are highlighted in bold.
Image Domain & Task
Al-Genorated Content (AIGC) SR
Comic Image SR
Dataset
Suxb-Turbo
DocpPloyd PSNR
Midjoumey
on Manga109
Methed
Year
PSNR
S
SSIM
PSNR 1 SSIM
PSNR
SSIM 1
Singlo-Task Method
MPRNet
CVPR2021
31,13
0.8730
26.76
0.8254
27.19
0.7656
27.13
0.8612
SwinIR
ICCVW2021
31.20
0.8709
27.41
27.19
0.7627
27.76
0.8697
Restormer
CVPR2022
32.95
0.8985
28.81
018675
28.24
0,8005
28.13
0.8758
NAFNet
ECCV2022
32.67
0.8959
28.59
0.8637
28.12
0.7965
27.98
0.8727
All-in-One Method
Transwather
CVPR2022
32.49
0.8916
27.73
0.8456
27.81
0.7866
26.00
0.8330
PromptI
NeurIP22023
32.38
0.8933
28.56
0.8642
27.90
0.7916
28.28
0.8756
AdaIR
ICLR2025
32.95
0.8989
28.87
0,8687
28.23
0,8008
28.05
0.8758
MoCEIR
CVPR2025
33,00
0.8995
28.90
0,8680
28.30
0.8027
28.10
0.8745
Muti-Domain All-in-One Method
DATPRL-IR (Ours)
33,25
0.9017
0.8713
0.8045
28.73
0,8828
Table 10: Quantitative zero-shot comparison between method and other SOTA methods on unseen image specular highlight removal, and image desnowing. The best metrics are highlighted in bold.
Highlight Removal
Image Desnowing on Snow 100K-S
Task Dataset
on SHIQ
Method
Year
PSNR
SSIM
PSNR
SSIM f
Single-Task Method
MPRNet
CVPR2021
25.83
0.9297
26.09
0.8882
Restormer
CVPR2022
25.67
0.9295
26.57
0.8955
NAFNet
ECCV2022
25.89
0,9298
26.27
0,8923
All-in-One Method
Transweather
CVPR2022
26.20
0.9315
26.36
0.8870
PromptIR
NeurlPS2023
24.87
0,9239
26.42
0,8923
AdaIR
ICLR2025
25.35
0,9263
26.54
0,8951
MoCEIR CVPR2025
25.30
0,9259
26.65
0.8976
All-in-One Method
DATPRI-IR (Ours)
26.22
0.9336
27.20
0.9018
our method can accurately remove degradation and reconstruct lost textures, while other methods can hardly remove any highlights.
Generalization to unseen distributions. We evaluate generalization performance on real-world image deraining and remote-sensing image (RSI) deblurring, both of which come from distribu- tions different from the training data. Real-world image deraining performance is tested on SPA- Data (Wang et al.] 2019). which has 1000 pairs of real-world test images. As for RSI deblur- ring, we randomly add varying degrees of Gaussian blur, defocus blur, and motion blur to the UCMerced (Yang & Newsam 2010) test set, creating 300 pairs of clean-blurred remote sensing images for testing. As shown in Table IT] our method comprehensively outperforms other AiOIR methods on both tasks. In particular, on the RSI deblurring task, it achieves an almost 2dB PSNR advantage over the SOTA methods MoCEIR, AdaIR, and PromptI. In addition, shown in Figure 13 the comparative methods struggle to effectively remove blur from images and have issues with task recognition errors, resulting in restored images with significant color shifts. In contrast, method can effectively remove blur while preserving colors and other image content.
The above experiments demonstrate the two learned prompt pools capture reusable domain and task priors rather than dataset-specific patterns. When tested on unseen domains, tasks, and distributions, model can adapt by selecting and composing prompts that best match the new data distribution, demonstrating clear transfcrability. This further proves the importance of modeling both shared knowledge and unique knowledge across different tasks/domains for image restoration.
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Table 11: Quantitative generalization comparison between our method and other SOTA methods on real-world image deraining and RSI deblurring. The best metrics are highlighted in bold.
Real World Deraining
RSI Deblurring
Task & Dataset
on SPA-Data
on UCMerced
Method
Year
PSNR 1
SSIM f
PSNR
SSIM
Single-Task Method
MPRNet
CVPR2021
31.99
0,9263
26.70
0.7741
Restormer
CVPR2122
32.35
0.9282
27.21
0.7928
NAFNet
ECCV2022
0,9249
26.92
0.7864
All-in-One Method
Transweather
CVPR20Z2
32.21
0.9304
26.60
0.7684
PromptIR
NeurIPS2023
32.09
0,9254
26.77
0.7828
AdaIR
ICLR2025
32.28
0,9278
27.22
0,7951
MoCEIR CVPR2025
32.20
0.9261
27.35
0.7956
Muti-Domain All-in-One Method
DATPRL-IR (Ours)
32.32
0.9265
28.91
0.8108
Vel.1
Vol.1
Vol.1
Vol.1
Vel.1
Fol.1
Input
PromptIR
(Ours)
Figure 10: Zero-shot comparison of our DATPRL-IR with other SOTA methods on AIGC and comic domains. Please zoom for better visualization.
D.3 PERCEPTUAL QUALITY COMPARISON
For more comprehensive evaluation, shown in Table 12 test five perceptual met- ricS-CLIPIQA (Wang et al.] (023b), MANIQA (Yang et al. 2022). MUSIQ (Ke et al. 2021). NIQE (Zhang al. 2015), and FID (Heusel al. Across all the three tasks, our DATPRL-IR consistently achieves the best scores in CLIPIQA and FID. In image deraining task, our method almost entirely outperforms other methods, achieving the best results on four metrics: CLIPIQA, MANIQA, and MUSIQ. and FID. Overall, our method achieves better or comparable perceptual scores compared existing methods. worth noting that to ensure fair and meaningful evaluation, we report these metrics only on natural-image tasks, be- cause CLIPIQA. MANIQA, and MUSIQ, etc. are pre-trained exclusively on natural-image datasets, and applying them medical or remote-sensing images would not provide reliable or interpretable scores. Moreover, medical and remote-sensing communities (Chen et al. 2025 Yang et al. 2024b a Lei et al. 2017: Wu et al. 2023) well AiOIR community (Potlapalli et al. [023]] Zamfir al. 2025 Cui et al. 2024; Tian et al,) 2025 Li et al. 2022) primarily focus more on fidelity-basee
the 3-domain and 9-task setting.
(pixel-leved metrics due to their optimization objective and safety requirements.
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Inpul
Restormee
PrompUR
AdalR
MOCEIR
DATPRL-IR (Ours)
Figure 11: Zero-shot comparison of our DATPRL-IR with other SOTA methods on specular high- light removal task. Please zoom for better visualization.
Input
DATPRI- (Ours)
Figure 12: Zero-shot comparison of our DATPRL-IR with other SOTA methods on image desnowing
task. Please zoom for better visualization.
E MORE ABLATION STUDIES
Effectiveness on different fusion strategies. To evaluate the effectiveness on different fusion strate- gies, except for our fusion strategy where the task prompt representation is combined with the do- main prompt representation and then further fused with the image features in the decoder layers, we implement another reasonable fusion strategy, domain-timage *task sequential fusion variant, for
Table 12: Perceptual quality comparison between our method and other SOTA methods on 3 do- mains & 9 tasks experimental setting. The best metrics are highlighted in bold
Iaage Dessan
Natora Image
Taak &
SR DIVIK
Denining
DeHlariig EaPPi
Duseet
Radel
Mathel
Yorr
CUPGAA
MANIAA MISSQ
NIDR
FIB
MANIOA MUSIO NIGH 1 1I01
CLIRDA 1. MANIDA MUSIQ" NigF L F1D
Tak Mathed
Swillt
B4512
015189
3.8681
$02.31
6/290
(18881
1863
3.155%
8.386
03142
04458
35.91
5,513
25.21
Reaemas
CV922122
B4534
05138
98.15
61088
102001
07543
077000
10.55
3.2066
6.3805
03586
044299
35.00
5,371
1716
NARNS
26.00
5.447
Allin-De Oue Nathol
CNTR2005
D4900
04835
31.55
6 1003
100.00
E/903
06682
68:7
3.4575
33,050
0 0.503
03332
33.81
61138
35:6
ProepilR
NouI1PS3033
0.5855
03995
$50.05
0.7545
06588
10.81
33141
7.5667
3385
0.393
36.90
S6221
UV/M22C
144864
051221
01882
002.90
10.55
3.3187
7.1322
0.599
37.80
5.3775
16:30
(Qaz)
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THe Hem
Th Kep PALAMBO BY
NA TETT
BA TEII
NA Ter
Input
Restormer
PromptiR
AdaIR
MOCEIR
DATPRL-IR (Ours)
GT
Figure 13: Generalization comparison of DATPRL-IR with other SOTA methods on RSI deblur- ring task. Please zoom in for better visualization.
Effectiveness on different fusion strategies domains & 9 tasks experimental setting. The best metrics are highlighted in bold.
Table 13:
Dutiset
Derzining Roin1OLL.
Debluming Golto
Denetsing AAPM
CUHK
Debazing RICEI
Method
PSNR
SSIM
Sequential Fusion (domain comnain--imae----e
0.9665
0.8832
28.27
0.7902
25.52
26.76
0.9376
0.9867
0.9273
0.7913
0.9347
comparison. We use cross- aross-attention mechanisms to sequentially fuse domain prompt representation with image features, and then fuse combined features with task prompt representation. shown in Table 13 although sequential fusion also achieved considerable performance, its performance still declined compared to our direct fusion. In MD-AiOIR, both domain prompt representation and task equally important. Our fusion method aims to learn prompt repre-
prompt representation sentation that is both task-aware and domain-aware to guide the restoration network. addition, compared sequential fusion, fusion method can also use the proposed adaptive gated fusion (AGF) to control the contribution ratio between backbone features and domain-awan task prompt representation, which is also very important.
24


Reviews:
[{"review_id": "ETL7nD4tPX", "reviewer": "Reviewer_5gy3", "summary": "The paper proposes DATPRL-IR, a multi-domain all-in-one image restoration framework that integrates both task-specific and domain-specific priors via a dual prompt-pool mechanism. It adaptively selects task and domain prompts, aligns domain prompts with textual features from LLaVA/CLIP, and fuses them through cross-attention to guide restoration. Experiments on 6- and 9-task, 3-domain setups show clear gains over SOTA AiOIR methods in PSNR/SSIM and generalization ability.", "questions": "For now I prefer a 4 rating (because no 5 option), but I will raise score if the weakness and the questions are properly addressed.\n\n1. How sensitive is the method to the choice of MLLM (e.g., LLaVA vs smaller models)?\n2. Are domain prompts reusable or transferable to unseen domains?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The problem scope is novel and interesting, from my point of view, this is the first attempt to unify AiOIR across multiple task domains.\n\n2. The dual prompt pools and cross-modal alignment are practically effective, and supported by comprehensive experiments with strong empirical performance and ablation studies.\n\n3. Figures and paper writings are easy to follow.", "weaknesses": "1. Although the evaluation scope contains multiple tasks from three domains, it is still unclear how the method will perform under zero-shot unseen domains. I think this is important to provide related experimental results to evaluate whether the proposed method is truely practical across domains.\n2. LLaVa seems generate captions that may exceed the 77 tokens limit of CLIP text encoder. Which part of the text is useful for cross modal alignment is worth exploration. \n3. The authors only provide PSNR/SSIM results for Table 1, lacking subjective metrics such as MANIQA/CLIPIQA etc., making it hard to fully validate the effectiveness of the proposed method.\n4. In abstract, the method is called \"DATRL-IR\" and \"DATPRL-IR\", which may be a typo.", "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": "ZFk5aYUwHp", "reviewer": "Reviewer_1CdE", "summary": "This paper proposes DATPRL-IR, a new method for image restoration across multiple domains (natural, medical, and remote sensing) and tasks, under the umbrella of what the authors term \"Multi-domain All-in-One Image Restoration\" (MD-AiOIR). The core idea is to utilize a dual prompt pool mechanism — task prompts and domain prompts — and fuse them via a prompt composition mechanism and cross-attention to guide the restoration process. The authors argue that DATPRL-IR is the first to tackle MD-AiOIR, claiming it represents a new research paradigm. Extensive experiments are conducted across 6 and 9 tasks on datasets from different domains.", "questions": "1. Were the compared methods (e.g., PromptIR, MoCE-IR) retrained on the same combined dataset of natural, medical, and remote sensing images? If not, how can we interpret the performance gap?\n2. How do the authors justify the claim that MD-AiOIR is a new research paradigm, rather than an empirical extension of existing AiOIR to more datasets?\n3. Given that medical and remote sensing datasets are relatively small in this paper, could the performance gains be due to overfitting or data imbalance?\n4. What are the broader implications of this work for the field of image restoration? Does the method generalize to unseen domains or tasks?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The implementation is comprehensive, involving a structured dual-prompt architecture with cross-modal alignment using LLMs (LLaVA/CLIP), regularization mechanisms, and adaptive fusion.\n2. Experiments are conducted on a relatively large number of tasks and datasets, covering natural, remote sensing, and medical images.\n3. Ablation studies and visualizations are detailed and well-presented, providing insights into the behavior of the model.", "weaknesses": "1. The central claim that \"Multi-domain All-in-One Image Restoration\" is a novel and meaningful research problem is not sufficiently justified. The distinction between MD-AiOIR and regular AiOIR is weak. The proposed setting is essentially a standard multi-task image restoration framework with more diverse datasets included. The fact that earlier works did not include medical or remote sensing datasets does not inherently make this a new problem. The paper does not provide a convincing motivation or theoretical/practical evidence that justifies treating MD-AiOIR as a distinct research direction.\n2. The paper fails to clarify whether the compared methods (e.g., PromptIR, MoCE-IR) were re-trained on the same multi-domain setting or simply evaluated out-of-domain. This is critical because these works were not originally designed for cross-domain tasks. Without a fair comparison, i.e., re-training all baselines on the same union of datasets, the reported superiority of DATPRL-IR is not convincing.\n3. The idea of using prompts (explicit or implicit) to guide image restoration is not new. Prior works such as PromptIR and related prompt-based multi-task models have explored similar mechanisms. While the dual-prompt pool design sounds well-structured, it is not fundamentally innovative. The use of CLIP or LLaVA to generate text features for domain alignment is incremental and does not offer deep conceptual novelty.\n4. Although the paper presents a complex architecture, it does not offer new insights into the nature of task/domain relationships in image restoration. There is little discussion on what has been learned, what the limitations are, or what implications this has for future research in AiOIR or multi-task learning more broadly.", "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": "3j6Fx8HnO0", "reviewer": "Reviewer_8geW", "summary": "The authors propose a novel method called DATPRL-IR, the first model for Multi-Domain All-in-One Image Restoration (MD-AiOIR), which aims to handle diverse restoration tasks across multiple image domains within a single network. Specifically, The author introduces Domain-Aware Task Prompt Representation Learning, which utilizes separate task and domain prompt pools to adaptively retrieve and combine knowledge, subsequently fusing them into a unified guidance signal for the restoration backbone. Extensive experiments demonstrate that DATPRL-IR significantly outperforms existing state-of-the-art methods on various tasks across natural, medical, and remote sensing image domains, showing strong performance and scalability as tasks are added.", "questions": "1. Fusion of domain and task prompts: The direct cross-attention between domain prompt features (PR_d) and task prompt features (PR_t) might not be the most effective fusion strategy. Given that domain and task information are conceptually orthogonal (one specifies the \"where,\" the other the \"what\"), directly fusing them could lead to information loss or unwanted interference. Would a sequential fusion strategy be more appropriate? For example, the domain prompt could first interact with the image features via cross-attention to produce domain-aware image features. These enriched features could then interact with the task prompts. This hierarchical approach might better preserve the distinct nature of both information types.\n\n2. The design of prompts: I would like to discuss the design of the prompt with the author: \nDomain Prompt Pool: The current strategy of relying on adjectives for domain identification appears potentially ambiguous. For instance, the descriptor \"black and white\" could apply to an old natural image or a medical image. This ambiguity might lead to inaccuracies in domain recognition. Have the authors considered using more explicit, categorical domain names as prompts, such as \"This is an MRI image\"? Such direct identifiers could provide a more robust and unambiguous signal for domain-aware guidance.\nTask Prompt Pool: The manuscript does not sufficiently elaborate on the concrete form or semantics of the task prompts. It remains unclear what kind of information they encapsulate (e.g., degradation patterns, desired output properties). To enhance clarity, could the authors provide a concrete example of what a task prompt might represent, either in the text or by annotating Figure 2? This would help readers better distinguish between the roles of task prompts and domain prompts.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The article is well-structured and easy to understand. By separating image types and restoration tasks, the author introduces an interesting approach to achieve different types of image restoration. This method avoids the confusion between types and tasks, allowing the model to better perform specific tasks in specific domains.", "weaknesses": "1. Motivation: The authors propose a unified image restoration model capable of handling various types of images. This raises several questions: Why is this approach necessary, and why were previous methods unable to achieve this? The authors briefly mention in the introduction that earlier methods did not accomplish such unification, but the author fails to analyze why those methods were incapable of doing so.\n\n2. How to hand multi-modal data inputs: The paper addresses multiple domains but does not explicitly detail how the model handles the significant variations in input data characteristics across these domains. For instance, MRI data is often complex-valued and may be stored as 2-channel data (real and imaginary parts), while natural images are typically 3-channel RGB data. How does the proposed method unify these different data types and channel dimensions to form a consistent input representation for the single backbone network? A clarification of this pre-processing or adaptation step is crucial for understanding the model's practical applicability and reproducibility.", "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": "pXxiNc0FIv", "reviewer": "Reviewer_mrLL", "summary": "This paper proposes **DATPRL-IR**, the first method for **Multi-Domain All-in-One Image Restoration (MD-AiOIR)**, which unifies diverse restoration tasks across natural, medical, and remote sensing image domains within a single model. The core idea is **Domain-Aware Task Prompt Representation Learning (DATPRL)**, which leverages two prompt pools: one for tasks (e.g., deraining, super-resolution) and one for domains (e.g., medical, remote sensing). Task prompts are learned end-to-end, while domain prompts are distilled from textual descriptions generated by a multimodal large language model (LLaVA) and aligned via CLIP. These prompts are adaptively selected and composed into instance-level representations, then fused via cross-attention and adaptive gating to guide the restoration backbone. Experiments on 6-task and 9-task settings across three domains show consistent improvements over state-of-the-art (SOTA) methods in PSNR and SSIM, with strong generalization and scalability.", "questions": "1. Could you clarify how the task prompts (keys and values) are initialized and updated during training? Are they fully learnable parameters?\n2. How is the task prompt representation $PR_t$ actually **integrated into the restoration network**? Is it used as a conditioning signal in attention layers, or elsewhere?\n3. Given the small performance gain from domain prompts, have you considered simpler domain encodings (e.g., one-hot vectors) as a baseline?\n4. Can you provide example LLaVA-generated captions for medical/remote sensing images? How diverse and informative are they?\n5. Have you compared your domain prompt pool against simpler alternatives (e.g., learnable domain embeddings)? Is the performance gain worth the added complexity?\n6. The paper claims “zero-shot capabilities”—can you show results on an unseen domain (e.g., train on natural + medical, test on remote sensing)?\n7. How sensitive is performance to the number of prompts (N=15) and top-k values (k=3/5)? Was this tuned extensively?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Novel problem setting**: MD-AiOIR is a meaningful extension beyond single-domain AiOIR.\n- **Thoughtful architecture**: The dual-prompt-pool design with adaptive composition and fusion is well-motivated.\n- **Comprehensive experiments**: Evaluation across three domains and up to nine tasks is thorough, with solid PSNR/SSIM gains over SOTA.", "weaknesses": "**Insufficient description of task prompt learning**: The paper states that the task prompt pool consists of key-value pairs $(K_j^{\\text{task}}, V_j^{\\text{task}})$ and that they are “optimized jointly with restoration objectives,” but it **does not clarify**:\n  - How the prompts are **initialized** (random? pre-trained?).\n  - Whether keys and values are **learned independently** or share structure.\n  - The **dimensionality and architecture** of the projector that maps features to the query.\n  - How the **value tensor** $V_j^{\\text{task}} \\in \\mathbb{R}^{T \\times d}$ is used in the restoration backbone (e.g., is it injected via cross-attention? added as bias?).\n  \n  Without these details, the **reproducibility and technical novelty** of the task prompt mechanism are hard to assess. In contrast, the domain prompt distillation pipeline is clearly explained.\n\n- **Limited gain from domain prompts**: As shown in Table 2, the domain prompt pool contributes only marginal improvements (e.g., +0.09 dB on CT denoising), raising questions about its necessity. Simpler domain encoding (e.g., one-hot vectors) might suffice.\n- **Reliability of LLaVA-generated descriptions**: Medical and remote sensing images often lack rich semantics. It’s unclear whether LLaVA can reliably generate meaningful, discriminative textual descriptions for such domains (e.g., “grayscale + human organs” may be too vague).\n- **Unverified zero-shot claims**: The abstract and conclusion mention “strong zero-shot capabilities,” but no experiments on unseen domains or tasks are provided to substantiate this.\n- **Minor typo**: The method is referred to as “DATRL-IR” in one sentence of the abstract, missing the “P”.", "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)