In contrast to close-set scenarios that restore images from a predefined set of degradations, open-set image restoration aims to handle the unknown degradations that were unforeseen during the pretraining phase, which is less-touched as far as we know. In this work, we explicitly study this challenging problem and reveal its essence, i.e., the unidentified distribution shifts between test and training data. In recent, test-time adaptation emerges as a fundamental method to address this inherent disparities. Inspired by this, we propose a test-time degradation adaption framework for open-set image restoration, which involves three components, i.e., i) a pre-trained and degradation-agnostic diffusion model to generate clean images, ii) a test-time degradation adapter adapts the unknown degradations based on the input image during the testing phase, and iii) the adapter-guided image restoration guides the model through the adapter to produce the corresponding clean image. Through experiments on multiple degradations absent from the training data, we show that our method achieves comparable even better performance than those task-specific methods.

翻译：与从预定义退化集合中复原图像的封闭场景不同，开放集图像复原旨在处理预训练阶段未预见的未知退化，据我们所知这一方向尚鲜有研究。本文明确研究这一挑战性问题并揭示其本质，即测试数据与训练数据之间未识别的分布偏移。近年来，测试时自适应成为解决这种固有差异的基础方法。受此启发，我们提出面向开放集图像复原的测试时退化适应框架，包含三个组件：i）预训练的退化无关扩散模型用于生成干净图像，ii）测试时退化适配器基于测试阶段输入图像适应未知退化，iii）适配器引导的图像复原通过适配器引导模型生成对应的干净图像。通过在多种训练数据中未出现的退化实验表明，我们的方法实现了与特定任务方法相当甚至更优的性能。