Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding

• URL: http://arxiv.org/abs/2411.16217v1
• Date: Mon, 25 Nov 2024 09:26:34 GMT
• Title: Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding
• Authors: Yubin Gu, Yuan Meng, Xiaoshuai Sun, Jiayi Ji, Weijian Ruan, Rongrong Ji,
• Abstract summary: Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degraded image restoration with a single model. In this paper, we propose a novel multiple-in-one IR model that can effectively restore images with both single and mixed degradations.
• Score: 67.57487747508179
• License:
• Abstract: Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degraded image restoration with a single model. However, in real-world scenarios, images often suffer from combinations of multiple degradation factors. Existing multiple-in-one IR models encounter challenges related to degradation diversity and prompt singularity when addressing this issue. In this paper, we propose a novel multiple-in-one IR model that can effectively restore images with both single and mixed degradations. To address degradation diversity, we design a Local Dynamic Optimization (LDO) module which dynamically processes degraded areas of varying types and granularities. To tackle the prompt singularity issue, we develop an efficient Conditional Feature Embedding (CFE) module that guides the decoder in leveraging degradation-type-related features, significantly improving the model's performance in mixed degradation restoration scenarios. To validate the effectiveness of our model, we introduce a new dataset containing both single and mixed degradation elements. Experimental results demonstrate that our proposed model achieves state-of-the-art (SOTA) performance not only on mixed degradation tasks but also on classic single-task restoration benchmarks.

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