Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance

2026-08-10

AI Summary

This work addresses the limitation of existing unified image restoration methods, which typically apply a single strategy globally and struggle to handle spatially varying degradation types and severities. To overcome this, we propose MGN-AIR, a novel framework that introduces, for the first time, a pixel-level multimodal guidance mechanism. By fusing textual semantics with visual cues, MGN-AIR enables precise perception and adaptive restoration of local degradations at the pixel level. The framework comprises three key components: pixel-wise visual prompt estimation, multimodal prompt fusion, and a prompt-guided restoration network, collectively enhancing fine-grained control over the restoration process. Extensive experiments demonstrate that MGN-AIR consistently outperforms state-of-the-art methods across multiple tasks, including denoising, deraining, deblurring, dehazing, desnowing, and low-light enhancement.

Abstract

All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model. Recent works have achieved remarkable progress by learning degradation-adaptive prompts or network architectures. However, these methods typically apply a uniform restoration strategy across the entire image, neglecting the fact that different regions may suffer from distinct degradation types and varying degrees of severity. In contrast, we propose to perform restoration at the pixel level, thereby enabling more fine-grained and precise control over the restoration process. Specifically, we present MGN-AIR, a novel pixel-level restoration framework for all-in-one image restoration. Our approach first learns to estimate a pixel-level visual prompt. Then, it leverages both textual and visual prompts to provide global and local degradation cues, guiding the model on where to look and how to restore at each pixel. We conduct extensive experiments on multiple all-in-one image restoration benchmarks, covering a wide range of tasks including denoising, deraining, deblurring, dehazing, desnowing, and low-light enhancement. Experimental results demonstrate that our proposed method consistently and significantly outperforms existing approaches.

Problem

• all-in-one image restoration
• pixel-level restoration
• degradation heterogeneity
• multimodal guidance
• uniform restoration

Innovation

• pixel-level restoration
• multimodal guidance
• all-in-one image restoration
• visual prompt
• degradation-adaptive