提出新模型统一修复单一与混合退化图像，效果领先。
Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding
- 用局部动态优化模块自适应处理不同退化区域。
- 引入条件特征嵌入提升混合退化场景修复能力。
- 在新数据集上表现最优，适合真实复杂退化场景。
多合一图像恢复（IR）已取得显著进展，旨在用单一模型处理所有单一退化类型。然而在真实场景中，图像常面临多种退化因素的组合。现有模型在应对混合退化时面临退化多样性与提示单一性挑战。本文提出一种新型多合一图像恢复模型，可有效修复单一及混合退化图像。为应对退化多样性，设计局部动态优化（LDO）模块，动态处理不同类型与粒度的退化区域；为解决提示单一性问题，提出高效条件特征嵌入（CFE）模块，引导解码器利用退化类型相关特征，显著提升混合退化恢复性能。为验证模型有效性，构建包含单一与混合退化元素的新数据集。实验表明，所提模型不仅在混合退化任务上达到最先进水平，且在经典单任务恢复基准测试中表现优异。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。