Existing unified methods typically treat multi-degradation image restoration as a multi-task learning problem. Despite performing effectively compared to single degradation restoration methods, they overlook the utilization of commonalities and specificities within multi-task restoration, thereby impeding the model's performance. Inspired by the success of deep generative models and fine-tuning techniques, we proposed a universal image restoration framework based on multiple low-rank adapters (LoRA) from multi-domain transfer learning. Our framework leverages the pre-trained generative model as the shared component for multi-degradation restoration and transfers it to specific degradation image restoration tasks using low-rank adaptation. Additionally, we introduce a LoRA composing strategy based on the degradation similarity, which adaptively combines trained LoRAs and enables our model to be applicable for mixed degradation restoration. Extensive experiments on multiple and mixed degradations demonstrate that the proposed universal image restoration method not only achieves higher fidelity and perceptual image quality but also has better generalization ability than other unified image restoration models. Our code is available at https://github.com/Justones/UIR-LoRA.

本文研究解决了现有统一方法在多降解图像修复中忽视任务间共性和特性的不足，提出了一种基于多重低秩适配器的通用图像修复框架。该框架利用预训练的生成模型共享组件，结合降解相似性的LoRA组合策略，显著提升了图像修复的保真度和感知质量，并在多种降解任务中展现出优越的泛化能力。

UIR-LoRA：通过多重低秩适应实现通用图像修复