arXiv:2505.18047v2 Announce Type: replace 
Abstract: The use of latent diffusion models (LDMs) such as Stable Diffusion has significantly improved the perceptual quality of All-in-One image Restoration (AiOR) methods, while also enhancing their generalization capabilities. However, these LDM-based frameworks suffer from slow inference due to their iterative denoising process, rendering them impractical for time-sensitive applications. Visual autoregressive modeling (VAR), a recently introduced approach for image generation, performs scale-space autoregression and achieves comparable performance to that of state-of-the-art diffusion transformers with drastically reduced computational costs. Moreover, our analysis reveals that coarse scales in VAR primarily capture degradations while finer scales encode scene detail, simplifying the restoration process. Motivated by this, we propose RestoreVAR, a novel VAR-based generative approach for AiOR that significantly outperforms LDM-based models in restoration performance while achieving over $10\times$ faster inference. To optimally exploit the advantages of VAR for AiOR, we propose architectural modifications and improvements, including intricately designed cross-attention mechanisms and a latent-space refinement module, tailored for the AiOR task. Extensive experiments show that RestoreVAR achieves state-of-the-art performance among generative AiOR methods, while also exhibiting strong generalization capabilities.

تقديم RestoreVAR يمثل تقدمًا كبيرًا في تكنولوجيا استعادة الصور. من خلال الاستفادة من النمذجة التلقائية البصرية، يتناول هذا النهج أوقات الاستدلال البطيئة المرتبطة بالنماذج التقليدية للانتشار الكامن، مما يجعله أكثر ملاءمة للتطبيقات الحساسة للوقت. لا يعزز هذا التطور جودة استعادة الصور فحسب، بل يوسع أيضًا استخدامه العملي، وهو أمر حاسم في مجالات مثل الوسائط الرقمية والتصوير الفوتوغرافي.

La introducción de RestoreVAR marca un avance significativo en la tecnología de restauración de imágenes. Al aprovechar la modelización autoregresiva visual, este enfoque aborda los lentos tiempos de inferencia asociados con los modelos de difusión latente tradicionales, haciéndolo más adecuado para aplicaciones sensibles al tiempo. Este desarrollo no solo mejora la calidad de la restauración de imágenes, sino que también amplía su uso práctico, lo cual es crucial en campos como los medios digitales y la fotografía.

L'introduction de RestoreVAR marque une avancée significative dans la technologie de restauration d'images. En s'appuyant sur la modélisation autoregressive visuelle, cette approche résout les temps d'inférence lents associés aux modèles de diffusion latente traditionnels, la rendant plus adaptée aux applications sensibles au temps. Ce développement améliore non seulement la qualité de la restauration d'images, mais élargit également son utilisation pratique, ce qui est crucial dans des domaines comme les médias numériques et la photographie.

The introduction of RestoreVAR marks a significant advancement in image restoration technology. By leveraging visual autoregressive modeling, this approach addresses the slow inference times associated with traditional latent diffusion models, making it more suitable for time-sensitive applications. This development not only enhances the quality of image restoration but also broadens its practical use, which is crucial in fields like digital media and photography.

RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration

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RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration

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