UnSCAR: Universal, Scalable, Controllable, and Adaptable Image Restoration

Debabrata Mandal; Soumitri Chattopadhyay; Yujie Wang; Marc Niethammer; Praneeth Chakravarthula

cs.CVMar 8, 2026

UnSCAR: Universal, Scalable, Controllable, and Adaptable Image Restoration

Authors: Debabrata Mandal, Soumitri Chattopadhyay, Yujie Wang, Marc Niethammer, Praneeth Chakravarthula

Organizations: University of North Carolina at Chapel Hill · University of California, San Diego

Abstract

Universal image restoration aims to recover clean images from arbitrary real-world degradations using a single inference model. Despite significant progress, existing all-in-one restoration networks do not scale to multiple degradations. As the number of degradations increases, training becomes unstable, models grow excessively large, and performance drops across both seen and unseen domains. In this work, we show that scaling universal restoration is fundamentally limited by interference across degradations during joint learning, leading to catastrophic task forgetting. To address this challenge, we introduce a unified inference pipeline with a multi-branch mixture-of-experts architecture that decomposes restoration knowledge across specialized task-adaptable experts. Our approach enables scalable learning (over sixteen degradations), adapts and generalizes robustly to unseen domains, and supports user-controllable restoration across degradations. Beyond achieving superior performance across benchmarks, this work establishes a new design paradigm for scalable and controllable universal image restoration.

Figures & tables

Figure 1: We introduce UnSCAR , a universal image restoration model that can handle $2\times$ more degradations than previously explored, achieving state-of-the-art performance across multiple benchmarks. Our framework also enables user-controllable restoration via degradation-control sliders and supports few-shot adaptation (down to one-shot) with strong out-of-distribution generalization on challenging medical imaging datasets.

Method	Venue	Unified Model	Mixed Restoration	Automatic	Scalability	Adaptability	Control Modality	#Primary Tasks
Restormer [ 80 ]	CVPR‘22	✗	✗	✗	✗	(✓)	—	1
AutoDIR [ 31 ]	ECCV‘24	✓	(✓)	(✓)	✗	✗	—	4
DA-CLIP [ 50 ]	ICLR‘24	✗	✗	✓	✗	✗	—	10
OneRestore [ 26 ]	ECCV‘24	✓	✓	(✓)	(✓)	✗	Limited	3
MPerceiver [ 5 ]	CVPR‘24	✓	✓	✓	✗	(✓)	—	8
CAPTNet [ 24 ]	TCVST‘24	✓	✗	✗	✗	✗	—	4

Table 1: Comparison with prior image restoration works. Factors are denoted as fully met ✓ , partially met (✓) , or unmet ✗ .

Panel of Figure 2 — Figure 2: Low-level cues. (a) Low-level cues provide discriminative signals across degradation types, enabling automatic restoration. (b) Our framework leverages diverse cues such as edges, color statistics, noise, and transmission, to characterize the input scene.

Figure 3: UnSCAR architecture. We leverage low-level visual guidance within a bidirectional feedback, controllable generative network with mixture-of-experts. This enables unified image restoration across multiple simultaneous degradations.

Figure 4: Degradation-aware embeddings. Our degradation-aware training framework (left) learns an auxilliary degradation encoder cloned from the CLIP image encoder trained with SigLIP loss [ 82 ] . The resulting embeddings provide clear separation between single- and mixed-degradation tasks while maintaining proximity across related degradations.

Figure 5: Single-degradation restoration. UnSCAR produces sharper, higher-fidelity reconstructions across single-degradation tasks.

Figure 6: Generalization to unseen degradations. Zero-shot evaluation of baseline methods and our approach on unseen mixed-degradation datasets.

		Dehazing: RESIDE [ 38 ]			Motion Deblur : RHM [ 84 ]			Low-Light: LOL [ 74 ]			Denoising : CBSD68 [ 52 ]
	Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
${\color[rgb]{0,0,1}\blacksquare}$	Restormer [ 80 ]	26.57	0.95	0.05	26.41	0.81	0.35	20.43	0.83	0.30	28.60	0.79	0.23
${\color[rgb]{0,0,1}\blacksquare}$	NAFNet [ 12 ]	27.62	0.96	0.04	27.87	0.84	0.24	22.19	0.88	0.16	32.71	0.89	0.08
${\color[rgb]{0.5,0.5,0}\blacksquare}$	PromptIR [ 59 ]	26.27	0.95	0.03	28.69	0.86	0.18	18.58	0.83	0.20	30.52	0.84	0.16
	OneRestore [ 26 ]	30.68	0.97	0.02	27.95	0.82	0.27	22.21	0.88	0.19	32.27	0.88	0.11
	InstructIR [ 14 ]	29.78	0.97	0.02	27.14	0.83	0.24	22.33	0.89	0.14	32.10	0.88	0.10

Table 2: Single-degradation restoration performance. All models are trained jointly on all tasks to evaluate scalability. Bold denotes best performance; underline indicates second best.

{{\color[rgb]{0,0,1}\blacksquare}}

and

{{\color[rgb]{0.5,0.5,0}\blacksquare}}

denotes prior single-task and all-in-one restoration models.

Figure 7: Mixed restoration. Zero-shot evaluation of baseline methods vs ours for mixed restoration dataset with individual tasks seen during training.

Figure 8: In-the-wild degradation removal. Comparison with the diffusion based DA-CLIP method on heavy indoor smoke removal [ 88 ] .

Method	TOLED [ 95 ]		POLED [ 95 ]		EUVP [ 30 ]
Method	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$
Restormer [ 80 ]	6.95	46.69	10.38	27.58	7.30	57.93
NAFNet [ 12 ]	6.90	49.58	6.65	34.42	6.76	50.37
OneRestore [ 26 ]	6.93	52.97	6.49	16.57	7.43	56.45
InstructIR [ 14 ]	7.16	49.05	7.06	35.08	6.83	50.89
AdaIR [ 16 ]	7.07	44.99	6.96	25.06	7.40	54.60

Table 3: Zero-shot performance on unseen-mixed (left) and mixed datasets (right). The best performing models across all tasks from Table 1 were used for evaluations.

Figure 9: Fewshot generalization. One (K=1) to Few (K<=50) shot generalization performance over demoiring and demosaicing tasks.

Table 13

Table 14

Figure 10: Medical. Our model adapts to out-of-distribution data such as medical images.

Figure 11: Controllability. Our model supports slider-based control of the level of restoration per degradation. $\alpha$ denotes the aberration strength (haze vs. rain, noise vs. gray).

Figure 12: Ablation studies. We analyze model performance under reduced sampling steps (left) and fewer low-level cues under automatic versus user controlled restoration (middle), and the impact of decoder refinement on structural preservation (right).

All-in-One Image Restoration

All-in-one image restoration trains unified models to remove diverse and co-occurring degradations while preserving scene content.

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Divide and Restore: A Modular Task-Decoupled Framework for Universal Image Restoration

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Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland

Image Restoration All-in-One Image Restoration Mixture-of-Experts Models

Open paper AskPDF arXiv

Restoring images affected by various types of degradation, such as noise, blur, or improper exposure, remains a significant challenge in computer vision. While recent trends favor complex monolithic all-in-one architectures, these models often suffer from negative task interference and require extensive joint training cycles on high-end computing clusters. In this paper, we propose a modular, task-decoupled image restoration framework based on an explicit diagnostic routing mechanism. The architecture consists of a lightweight Convolutional Neural Network (CNN) classifier that evaluates the input image and dynamically directs it to a specialized restoration node. A key advantage of this framework is its model-agnostic extensibility: while we demonstrate it using three independent U-Net experts, the system allows for the integration of any restoration method tailored to specific tasks. By isolating reconstruction paths, the framework prevents feature conflicts and significantly reduces training overhead. Unlike monolithic models, adding new degradation types in our framework only requires training a single expert and updating the router, rather than a full system retraining. Experimental results demonstrate that this computationally accessible approach offers a scalable and efficient solution for multi-degradation restoration on standard local hardware. The code will be published upon paper acceptance.

Abstract

All-in-One Image Restoration

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