统一图像修复模型通过分层估计退化粒度，提升修复精度。
UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity
- 分层聚类退化空间，构建多粒度专家混合模型。
- 在多个数据集上超越现有全功能修复方法，接近专用模型性能。
- 适配退化与粒度估计，减少误判影响，适合复杂场景修复。
近年来，全功能图像修复取得显著进展。现有方法分为退化无关与退化感知两类：前者无法利用特定退化信息，后者则受限于退化估计误差。为此，本文提出UniRestorer，通过在退化空间进行分层聚类，训练多粒度专家混合（MoE）修复模型，并同时估计退化类型与粒度，自适应选择最优专家进行修复。相比传统退化无关或感知方法，UniRestorer既能利用退化信息实现针对性修复，又通过粒度估计增强对退化估计误差的鲁棒性。实验表明，该方法显著优于当前最先进的全功能修复模型，在多个数据集上表现接近专用单任务模型。
原文摘要 · Abstract (English)
Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in leveraging degradation-specific restoration, and the latter suffer from the inevitable error in degradation estimation. Consequently, the performance of existing methods has a large gap compared to specific single-task models. In this work, we make a step forward in this topic, and present our UniRestorer with improved restoration performance. Specifically, we perform hierarchical clustering on degradation space, and train a multi-granularity mixture-of-experts (MoE) restoration model. Then, UniRestorer adopts both degradation and granularity estimation to adaptively select an appropriate expert for image restoration. In contrast to existing degradation-agnostic and -aware methods, UniRestorer can leverage degradation estimation to benefit degradation specific restoration, and use granularity estimation to make the model robust to degradation estimation error. Experimental results show that our UniRestorer outperforms state-of-the-art all-in-one methods by a large margin, and is promising in closing the performance gap to specific single task models.
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