PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

Lingchen Sun; Rongyuan Wu; Xiangtao Kong; Jixin Zhao; Qiaosi Yi; Yujing Sun; Shuaizheng Liu; Zhengqiang Zhang

cs.CVAug 17, 2026

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

Authors: Lingchen Sun, Rongyuan Wu, Xiangtao Kong, Jixin Zhao, Qiaosi Yi, Yujing Sun, Shuaizheng Liu, Zhengqiang Zhang, +1 more

Organizations: The Hong Kong Polytechnic University · OPPO Research Institute

Abstract

Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors. However, the variational autoencoder (VAE) in latent T2I models may discard restoration-sensitive details, while the open-ended synthesis prior can introduce content-inconsistent artifacts. We present PixRestore, a VAE-free pixel-space Diffusion Transformer (DiT) for UIR, where the diffusion backbone is trained entirely from scratch, without relying on T2I pretraining. PixRestore performs flow matching directly on patchified pixels, preserving fine-grained details while keeping the token sequence tractable. To adapt to different degradations, PixRestore learns to predict the reliability of layer features using LQ--HQ DINO feature similarity. Features from more reliable layers are fused as dense conditioning, while less reliable layers receive stronger HQ-feature supervision to encourage degradation removal. We train PixRestore on a large-scale corpus of diverse scenes and degradations, and further finetune it into a one-step generator using DINO-based adversarial objectives for efficient inference. Experiments on public benchmarks and real-world test sets show that, with only about 50M parameters and single-step inference, PixRestore achieves the best overall fidelity, perceptual quality, and robustness to degradations among competing UIR models while being far more efficient. Larger PixRestore variants can further boost performance, demonstrating the scalability of our pixel-space design. Code and the curated benchmark can be found at https://github.com/csslc/PixRestore.

Figures & tables

Figure 1: PixRestore achieves the best overall performance in terms of restoration quality, model size, and inference speed. Top-left: radar charts comparing PSNR, LPIPS, and degradation removal performance across eight degradation types. Top-right: GFLOPs versus inference latency, where the bubble size denotes the number of parameters. Bottom: visual comparisons on eight restoration tasks. With only about 50M parameters and single-step inference, PixRestore achieves the best overall restoration quality while being the most efficient among diffusion-based methods.

Figure 2: Overview of PixRestore. A frozen vision encoder extracts multi-layer features from the LQ image, and an adaptive layer router predicts per-layer weights to fuse them into a single conditioning feature. The LQ image and the noisy state $x_{t}$ are patchified, then processed by $N$ DiT blocks, and finally decoded into the HQ output.

Model	VAE	Params(M)	Inf Time-DM (ms/step)	Inf Time-VAE (ms)	PSNR (dB) $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$
Latent DiT-S	SD2VAE (f8c4, ps1)	106.41	25	78	22.10	0.2483	50.38
Latent DiT-S	FluxVAE (f8c16, ps1)	106.59	25	78	22.63	0.2109	50.86
Latent DiT-S	QwenVAE (f8c16, ps1)	67.37	25	41	22.80	0.2181	51.87
Pixel DiT-S	None (ps8)	23.41	25	0	26.62	0.1593	54.32

Table 1: Latent diffusion vs. pixel diffusion under the same UIR training/test setting. The results are averaged over 8 restoration tasks. Pixel-space modeling provides a better overall trade-off in fidelity, perceptual quality, parameter count, and inference speed.

Figure 3: Motivation of adaptive hierarchical visual guidance. Left: Per-layer DINO feature visualizations for low-light enhancement and de-raindrop. Shallow layers preserve local structures and details, while deeper layers encode global semantics. Right: LQ–HQ feature similarity across DINOv2-B layers for eight types of degradations. We see that different layers are sensitive to different degradations.

Figure 4: No-reference quality metrics do not reliably reflect degradation removal. Here PixRestore removes the rainstreaks best, yet MUSIQ and AFINE-NR rank it worst, favoring the LQ input and the degradation-preserving output of FoundIR-v2, while our VLM-based DR-Score demonstrates strong alignment with human perceptual judgments.

Method	De-rainstreak					Denoise					Deblur					De-raindrop
	PSNR	SSIM	LPIPS	DISTS	DR-Score	PSNR	SSIM	LPIPS	DISTS	DR-Score	PSNR	SSIM	LPIPS	DISTS	DR-Score	PSNR	SSIM	LPIPS	DISTS	DR-Score
PromptIR	24.50	0.7507	0.3529	0.2381	31.25	32.54	0.9029	0.2459	0.1604	55.35	23.82	0.7584	0.3131	0.2195	27.23	18.81	0.6962	0.3390	0.1885	24.86
PromptIR ∗	28.43	0.8389	0.2659	0.1904	49.99	35.45	0.9377	0.1104	0.1065	79.33	29.10	0.8474	0.2051	0.1560	55.56	23.68	0.8005	0.2236	0.1326	51.74
DiffUIR	24.51	0.7618	0.3484	0.2248	40.13	26.61	0.8380	0.3103	0.2056	59.52	27.86	0.8200	0.2258	0.1699	44.09	18.85	0.7004	0.3455	0.1905	26.50
UniRestore	22.50	0.7291	0.4223	0.2689	34.31	31.75	0.8969	0.2336	0.1669	61.20	23.87	0.7205	0.2405	0.1716	46.94	18.50	0.6377	0.4114	0.2285	27.90
DA-CLIP	24.50	0.7560	0.3347	0.2176	43.09	27.35	0.8245	0.2459	0.1764	64.12	27.40	0.8113	0.1641	0.1303	55.00	20.12	0.6988	0.2715	0.1512	55.20

Table 2: Quantitative comparison on public benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained using the same training dataset as ours. Metrics: PSNR

\uparrow

, SSIM

\uparrow

, LPIPS

\downarrow

, DISTS

\downarrow

, DR-Score (degradation-removal score)

\uparrow

Figure 5: Visual comparisons on desnow (top) and low-light enhancement (bottom). PixRestore removes degradations effectively and recovers more faithful details and colors.

Method	NFE	Params (M)	FLOPs (G)	Latency (ms)
PromptIR	-	35.59	1382	334
DiffUIR	4	36.26	3839	777
DA-CLIP	100	231.76	112927	18071
FoundIR	4	36.26	3839	777
UniRestore	1	1071.20	5255	158
FoundIR-v2	20	16910.09	119334	18293

Table 3: Complexity comparison of different methods. “NFE” denotes number of evaluations. All values are measured with input

1\times 3\times 512\times 512

on a single NVIDIA A800 GPU, with 5 warmup iterations and averaged over 100 runs.

ID	Variant	NFE	Conditioning	Supervision	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$
A0	Pixel DiT-S	10	None	None	26.62	0.8454	0.1593	54.32
A1	+ single-layer conditioning	10	layer 2	None	27.07	0.8457	0.1561	54.45
A2	+ single-layer conditioning	10	layer 5	None	27.36	0.8487	0.1489	54.64
A3	+ single-layer conditioning	10	layer 11	None	27.12	0.8491	0.1508	54.60
A4	+ multi-layer conditioning	10	Avg. 2 layers	None	27.62	0.8540	0.1412	54.90
A5	+ multi-layer conditioning	10	Avg. 6 layers	None	27.72	0.8536	0.1407	55.01

Table 4: Ablation studies of PixRestore. “Avg.” denotes uniform averaging of selected DINO features for conditioning or supervision, while “Adap.” denotes adaptive averaging of selected DINO features for conditioning or supervision. “NFE” denotes the number of evaluations.

Scheme	Training iterations	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$
Regression Training	350k	27.00	0.8179	0.1494	52.00
Flow pretraining + one-step finetuning (ours)	250k + 100k	28.49	0.8589	0.1120	55.52

Table 5: Comparison between diffusion pretraining and finetuning with regression training under the same objective and total training iterations.

Figure 6: Scaling behavior of PixRestore under varying backbone GFLOPs and patch sizes.

Degradation	Metric	PromptIR	PromptIR ∗	DiffUIR	DA-CLIP	DA-CLIP ∗	FoundIR	FoundIR ∗	UniRestore	FoundIR-v2	FoundIR-v2 ∗	Flux-IR	Flux-IR ∗	FAPE-IR	FAPE-IR ∗	PR	PR-B
De-rainstreak	MUSIQ $\uparrow$	59.90	60.06	61.02	62.23	59.94	61.00	60.87	62.58	63.06	62.57	62.98	60.23	61.07	59.53	62.62	62.78
	Affine-NR $\downarrow$	-0.90	-0.90	-0.92	-0.91	-0.91	-0.90	-0.92	-0.89	-0.96	-0.96	-0.94	-0.89	-1.00	-1.00	-1.02	-1.03
	DR-Score $\uparrow$	30.85	30.05	33.77	33.62	51.68	31.48	38.83	32.87	49.07	71.72	25.50	28.37	64.73	76.32	72.12	74.69
Deblur	MUSIQ $\uparrow$	34.28	32.57	41.64	46.61	43.92	32.85	34.10	49.26	69.99	72.78	55.26	65.73	44.89	45.84	53.37	56.56
	Affine-NR $\downarrow$	-0.73	-0.70	-0.78	-0.77	-0.76	-0.70	-0.72	-0.81	-1.04	-1.10	-0.87	-1.00	-0.80	-0.83	-0.88	-0.93
	DR-Score $\uparrow$	26.56	31.08	28.12	48.78	45.22	26.58	29.35	46.77	66.66	74.94	38.09	72.18	63.35	65.41	65.43	73.08

Table 6: Quantitative comparison on real-world test set. The best and second-best results for each metric are highlighted in red bold and blue italic , respectively. Retrained methods are marked with ∗ . ‘PR’ denotes the proposed PixRestore, the results of which are shaded in pink.

Figure 7: Visual comparisons on real-world desnow, dehaze, and deblur cases. PixRestore can remove degradations more effectively and recover cleaner details and sharper structures with fewer artifacts.

Figure 8: Visual comparisons on real-world de-raindrop, low-light enhancement, and de-rainstreak cases. PixRestore can remove degradations more effectively and recover cleaner details and sharper structures with fewer artifacts.

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Degradation	Training data
Deblur	GoPro [ 33 ] , RealBlur [ 57 ] , UHD-Blur [ 3 ] , LSD-Defocus [ 58 ]
Dehaze	RESIDE [ 2 ] , UHD-Haze [ 3 ] , WeatherBench-haze [ 41 ] , UniSer-Haze [ 59 ] , synthetic data from UrbanSyn [ 60 ] , BlendedMVS [ 61 ] and MegaDepth [ 62 ]
De-raindrop	RaindropClarity [ 63 ] , RainDS-Real-RainDrop [ 36 ] ,
de-rainstreak	Rain13K [ 64 ] , RealRain-1k [ 37 ] , UAV-Rain1k [ 38 ] , FoundIR-rain [ 9 ] , RainDS-Real-RainStreak [ 36 ] , synthetic data from UrbanSyn [ 60 ] ,
Desnow	Snow100K [ 65 ] , WeatherBench-snow [ 41 ] , synthetic data from UrbanSyn [ 60 ] ,
Denoise	SIDD [ 66 ] , PolyU [ 35 ] , synthetic Gaussian noise DF2K [ 34 , 67 ]

Appendix

Table S.1: Training data sources for each degradation type.

Degradation	Real-world test data
Deblur	GyroBlur-Real [ 70 ]
Dehaze	RTTS and OpenReal-fog [ 44 ]
De-raindrop	OpenReal-raindrop [ 44 ]
De-rainstreak	OpenReal-rainstreak [ 44 ] and DiffUIR [ 10 ]
Desnow	Snow100K-realistic [ 65 ] and OpenReal-snow [ 44 ]
Low-light

Appendix

Table S.2: Real-world test data sources for each degradation type.

Degradation	Latent DiT with FLUX-VAE	Latent DiT with Qwen-VAE	Latent DiT with SD2-VAE	Pixel DiT
SR	25.59/0.2038/58.68	25.62/ 0.1992 / 62.25	25.03/0.2586/58.53	26.48 /0.2067/57.80
Deblur	26.66/0.1617/46.53	26.93/ 0.1545 / 46.69	26.00/0.1921/45.23	27.17 /0.1850/41.76
Dehaze	15.31/0.2253/54.56	15.16/0.2391/52.50	15.08/0.2659/53.31	24.78 / 0.1020 / 58.17
Denoise	31.97/ 0.0952 /47.22	32.33/0.1021/48.37	30.65/0.1252/47.90	32.58 /0.1179/ 51.32
De-raindrop	20.43/0.2257/62.79	20.63/0.2463/64.71	20.01/0.2751/62.46	23.19 / 0.1707 / 66.66
De-rainstreak	25.07/0.1478/46.49	25.34/0.1821/47.63	24.53/0.1876/46.00	30.19 / 0.1350 / 49.43

Appendix

Table S.3: Pixel-space and latent-space comparison on 8 degradation types. We report comparisons on PSNR

\uparrow

/LPIPS

\downarrow

/MUSIQ

\uparrow

Visual Prior	PSNR (dB) $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$
CLIP-B	26.61	0.8441	0.1598	54.30
MAE-B	26.70	0.8454	0.1593	54.20
SigLIP-B	26.66	0.8449	0.1592	54.19
DINOv2-B	27.25	0.8500	0.1531	54.21

Appendix

Table S.4: Comparison of frozen visual foundation priors under the same pixel DiT setting. All encoders share the ViT-B architecture and parameter count. Features of the same layer are selected. Metrics are averaged over 8 degradation types. Best results are highlighted in bold .

Figure S.1: Illustration of DR-Score. Given the LQ input, the restored result, and the task description, the VLM judges whether the target degradation has been removed. A better restoration receives a higher DR-Score.

Figure S.2: Human alignment analysis of different no-reference metrics with pairwise human preferences. Left: alignment ratios between metric-induced rankings and human judgments across real-world UIR tasks. Right: representative failure cases where two restored results are visually similar with subtle appearance differences but lead to disagreement between DR-Score and human judgment.

Method	GoPro						UHD-blur
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	23.43	0.7903	0.3035	0.1972	25.50	-0.5874	24.21	0.7265	0.3228	0.2418	29.81	-0.6163
PromptIR ∗	29.82	0.8775	0.2000	0.1440	36.18	-0.7123	28.39	0.8172	0.2101	0.1681	40.52	-0.7570
DiffUIR	29.32	0.8669	0.2041	0.1492	35.93	-0.7137	26.39	0.7731	0.2476	0.1907	38.01	-0.7075
UniRestore	24.11	0.7496	0.2216	0.1408	45.93	-0.7896	23.63	0.6914	0.2595	0.2024	42.99	-0.6870
DA-CLIP	28.57	0.8554	0.1279	0.0994	41.05	-0.7329	26.23	0.7671	0.2003	0.1612	42.83	-0.7479

Appendix

Table S.5: Detailed quantitative comparison on Deblur benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Method	RESIDE-6K						UHD-Haze
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	26.69	0.9572	0.0460	0.0418	51.24	-0.9422	15.98	0.8035	0.2391	0.1605	63.94	-0.9765
PromptIR ∗	24.01	0.9417	0.0642	0.0553	51.58	-0.9362	18.49	0.8445	0.2054	0.1216	63.45	-1.0058
DiffUIR	24.66	0.9311	0.0708	0.0582	50.59	-0.9314	16.15	0.8001	0.2572	0.1768	62.57	-0.9683
UniRestore	23.63	0.9162	0.1171	0.0869	55.68	-0.9630	16.59	0.7776	0.3073	0.1849	62.14	-0.9155
DA-CLIP	28.66	0.9273	0.0559	0.0463	53.52	-0.9784	17.27	0.8230	0.2063	0.1307	65.88	-1.0698

Appendix

Table S.6: Detailed quantitative comparison on Dehaze benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Method	DIV2K (Gaussian)						PolyU
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	34.57	0.9079	0.1411	0.1384	64.24	-0.9098	30.50	0.8978	0.3506	0.1825	30.88	-0.6068
PromptIR ∗	33.91	0.8975	0.1463	0.1388	61.00	-0.8517	37.00	0.9780	0.0744	0.0742	32.70	-0.6156
DiffUIR	21.24	0.7540	0.3437	0.2523	55.21	-0.7013	31.98	0.9221	0.2769	0.1588	32.67	-0.6270
UniRestore	30.38	0.8728	0.1659	0.1420	64.25	-0.8868	33.11	0.9209	0.3012	0.1918	31.60	-0.5629
DA-CLIP	28.91	0.7532	0.2412	0.1764	57.33	-0.8155	25.79	0.8957	0.2506	0.1765	33.00	-0.6137

Appendix

Table S.7: Detailed quantitative comparison on Denoise benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Method	RainDS-real						RealRain-1K
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	25.15	0.7483	0.2047	0.1392	60.54	-0.8602	23.85	0.7531	0.5011	0.3370	42.82	-0.6698
PromptIR ∗	26.62	0.7933	0.1977	0.1254	61.79	-0.8963	30.23	0.8845	0.3341	0.2554	36.49	-0.5757
DiffUIR	26.11	0.7888	0.1885	0.1168	63.88	-0.9157	22.91	0.7348	0.5084	0.3329	45.21	-0.7440
UniRestore	23.47	0.7143	0.3220	0.1838	65.71	-0.8797	21.52	0.7439	0.5226	0.3540	45.97	-0.7192
DA-CLIP	24.66	0.7467	0.1834	0.1213	63.20	-0.9041	24.35	0.7654	0.4861	0.3139	46.39	-0.7354

Appendix

Table S.8: Detailed quantitative comparison on De-rainstreak benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Method	RainDS-real						UAV-Rain1k
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	20.70	0.7069	0.2773	0.1560	55.31	-0.8088	16.92	0.6855	0.4007	0.2211	66.57	-0.8022
PromptIR ∗	24.53	0.7529	0.2637	0.1347	59.14	-0.8560	22.84	0.8482	0.1835	0.1305	68.19	-0.8475
DiffUIR	20.56	0.6996	0.3119	0.1678	56.28	-0.8034	17.13	0.7011	0.3790	0.2132	66.67	-0.7942
UniRestore	20.25	0.6906	0.3482	0.1888	58.44	-0.8111	16.75	0.5847	0.4746	0.2683	65.96	-0.7044
DA-CLIP	22.99	0.7018	0.1763	0.0977	61.66	-0.8832	17.26	0.6958	0.3667	0.2047	67.27	-0.8080

Appendix

Table S.9: Detailed quantitative comparison on De-raindrop benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Method	UHD-LL						LOL
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	11.82	0.5660	0.5088	0.3116	35.06	-0.6577	9.17	0.3902	0.5732	0.4415	38.49	-0.7859
PromptIR ∗	25.33	0.8846	0.2303	0.1757	45.85	-0.6794	10.50	0.4916	0.4824	0.3268	44.53	-0.8607
DiffUIR	17.67	0.5064	0.6057	0.3320	37.19	-0.4779	25.76	0.9099	0.1581	0.1264	68.79	-0.9938
UniRestore	12.41	0.6102	0.4665	0.2903	36.98	-0.6417	9.48	0.4274	0.5347	0.3426	43.85	-0.7569
DA-CLIP	20.51	0.7436	0.3675	0.2201	48.51	-0.6172	23.99	0.8395	0.1263	0.1043	74.18	-1.0050

Appendix

Table S.10: Detailed quantitative comparison on Low-light Enhancement benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Method	WeatherBench
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	22.26	0.7939	0.2452	0.1672	45.60	-0.6023
PromptIR ∗	29.32	0.8532	0.1818	0.1402	46.30	-0.6161
DiffUIR	22.95	0.7948	0.2392	0.1667	48.10	-0.6185
UniRestore	22.33	0.7863	0.2464	0.1771	49.39	-0.6451
DA-CLIP	23.60	0.7971	0.2221	0.1558	46.48	-0.6127

Appendix

Table S.11: Detailed quantitative comparison on Desnow benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Method	RealSR						ScreenSR
	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	DISTS $\downarrow$	MUSIQ $\uparrow$	AFINE-NR $\downarrow$
PromptIR	23.47	0.7380	0.4647	0.2670	25.95	-0.4435	25.05	0.7365	0.4140	0.2351	42.95	-0.7419
PromptIR ∗	28.65	0.8051	0.3045	0.2382	45.99	-0.6821	26.54	0.7884	0.2633	0.2033	58.41	-0.8518
DiffUIR	27.40	0.7734	0.3744	0.2417	35.15	-0.4999	25.68	0.7431	0.3989	0.2338	48.34	-0.7720
UniRestore	24.84	0.7683	0.3358	0.2305	39.91	-0.5857	24.76	0.7403	0.3737	0.2263	53.30	-0.8091
DA-CLIP	24.21	0.7258	0.3910	0.2469	30.60	-0.4804	23.24	0.6321	0.3635	0.2265	47.89	-0.7682

Appendix

Table S.12: Detailed quantitative comparison on Super-resolution benchmarks. The best and second-best results are highlighted in red and blue italic , respectively. Methods marked with ∗ are retrained under the same training setting as ours.

Figure S.3: Visual comparisons on synthetic desnow, low-light enhancement, de-rainstreak, and denoise cases. Overall, PixRestore restores cleaner and more faithful results with better structural details and fewer artifacts.

Figure S.4: Visual comparisons on synthetic dehaze, de-raindrop, deblur, and SR cases. Overall, PixRestore restores cleaner and more faithful results with better structural details and fewer artifacts.

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Aug 7, 2026

Bend the Basics: Degradation-Aware Deformable Tokenization for All-in-One Image Restoration

All-in-one image restoration seeks a single model that can recover images degraded by diverse and spatially non-uniform corruptions. However, many unified Transformers rely on fixed patch partitioning: task/degradation condition is injected only into the backbone blocks after tokenization, leaving the embedding and reconstruction stages insensitive to local degradation variations. In contrast to previous approaches, we present Flexible Image Transformer (FIT) that explicitly models degradation awareness across the entire pipeline, from patch sampling to pixel reconstruction. Specifically, FIT employs a lightweight Degradation Encoder to predict a global degradation vector

\mathbf{g}

and a spatial degradation map

\mathbf{M}

from local degradation severity, which jointly condition the patch embedding and unembedding through adaptive deformation. Moreover, to improve robustness across degradation types, we introduce a task-token dropout strategy that regularizes task conditioning during training. On five standard benchmarks (BSD68, Rain100L, SOTS, GoPro, and LOLv1), FIT achieves state-of-the-art performance with 30.72 dB average PSNR on the five-degradation setting and 32.83 dB on the three-degradation setting, outperforming recent unified restoration methods by +0.5

\sim

1.1 dB. Moreover, the learned offsets provide a direct handle for visualizing degradation-aware spatial adaptation.

Zihao He, Yunfeng Wu, Xinchao Wang +1

School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China · Alibaba Group, Beijing, China · School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China +1

Image Corruption Robustness All-in-One Image Restoration Image Restoration

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Jun 16, 2026

Universal Image Restoration via Internalized Chain-of-Thought Reasoning

Image restoration seeks to recover high-quality images from degraded inputs but becomes highly ill-posed under complex, mixed degradations. While unified all-in-one models are common, their performance declines as degradation complexity increases. Recent works adopt Chain-of-Thought (CoT) reasoning for multi-round restoration using specialized modules. However, this approach faces two key limitations: (i) increased computational cost due to multi-step processing, and (ii) weak modeling of interactions between degradations during stepwise inference. We introduce CoTIR, a universal image restoration framework that internalizes CoT reasoning within a single model. Concretely, we view image restoration as a specialized subtask of image editing, which implies that a large-scale pre-trained editing model provides a more favorable optimization starting point. Building on this, we fine-tune the model for restoration and further encode structured CoT-style reasoning into the learning objective via a differentiable formulation inspired by Lagrangian optimization, enabling holistic restoration without chaining specialized restorers. To facilitate training and evaluation, we further present CoTIR-Bench, a large-scale benchmark comprising 5.2 million samples with CoT-style reasoning traces. Extensive experiments on CoTIR-Bench and broad real composite degradation scenes show that CoTIR achieves stronger perceptual quality and more competitive fidelity than both all-in-one models and multi-round restoration methods. The source code is available at https://github.com/gy65896/CoTIR.

Yu Guo, Zhengru Fang, Shengfeng He +4

Hong Kong JC Lab of Smart City and the Department of Computer Science, City University of Hong Kong · School of Computing and Information Systems, Singapore Management University · Computer Science and Information Engineering, National Taiwan University

All-in-One Image Restoration Image Restoration

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Apr 10, 2026

RIRF: Reasoning Image Restoration Framework

Universal image restoration (UIR) aims to recover clean images from diverse and unknown degradations using a unified model. Existing UIR methods primarily focus on pixel reconstruction and often lack explicit diagnostic reasoning over degradation composition, severity, and scene semantics prior to restoration. We propose Reason and Restore (R&R), a novel framework that integrates structured Chain-of-Thought (CoT) reasoning into the image restoration pipeline. R&R introduces an explicit reasoner, implemented by fine-tuning Qwen3-VL, to diagnose degradation types, quantify degradation severity, infer key degradation-related factors, and describe relevant scene and object semantics. The resulting structured reasoning provides interpretable and fine-grained diagnostic priors for the restorer. To further improve restoration quality, the quantified degradation severity produced by the reasoner is leveraged as reinforcement learning (RL) signals to guide and strengthen the restorer. Unlike existing multimodal LLM-based agentic systems that decouple reasoning from low-level vision tasks, R&R tightly couples semantic diagnostic reasoning with pixel-level restoration in a unified framework. Extensive experiments across diverse UIR benchmarks demonstrate that R&R achieves state-of-the-art performance while offering unique interpretability into the restoration process.

Wending Yan, Rongkai Zhang, Kaihua Tang +2

Southwest Jiaotong University, Chengdu, China · Nanyang Technological University, Singapore · Tongji University, Shanghai, China +2

Image Restoration All-in-One Image Restoration VLM Reasoning

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