Bridging Degradation Discrimination and Generation for Universal Image Restoration

• URL: http://arxiv.org/abs/2602.00579v1
• Date: Sat, 31 Jan 2026 07:46:28 GMT
• Title: Bridging Degradation Discrimination and Generation for Universal Image Restoration
• Authors: JiaKui Hu, Zhengjian Yao, Lujia Jin, Yanye Lu,
• Abstract summary: This paper presents a novel approach, Bridging Degradation discrimination and Generation (BDG)<n>We propose the Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS-GLCM) and demonstrate its effectiveness in performing fine-grained discrimination of degradation types and levels.<n>The objective is to preserve the diffusion model's capability of restoring rich textures while simultaneously integrating the discriminative information from the MAS-GLCM into the restoration process.
• Score: 18.085443590549087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Universal image restoration is a critical task in low-level vision, requiring the model to remove various degradations from low-quality images to produce clean images with rich detail. The challenges lie in sampling the distribution of high-quality images and adjusting the outputs on the basis of the degradation. This paper presents a novel approach, Bridging Degradation discrimination and Generation (BDG), which aims to address these challenges concurrently. First, we propose the Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS-GLCM) and demonstrate its effectiveness in performing fine-grained discrimination of degradation types and levels. Subsequently, we divide the diffusion training process into three distinct stages: generation, bridging, and restoration. The objective is to preserve the diffusion model's capability of restoring rich textures while simultaneously integrating the discriminative information from the MAS-GLCM into the restoration process. This enhances its proficiency in addressing multi-task and multi-degraded scenarios. Without changing the architecture, BDG achieves significant performance gains in all-in-one restoration and real-world super-resolution tasks, primarily evidenced by substantial improvements in fidelity without compromising perceptual quality. The code and pretrained models are provided in https://github.com/MILab-PKU/BDG.

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