Recent image restoration methods have produced significant advancements using deep learning. However, existing methods tend to treat the whole image as a single entity, failing to account for the distinct objects in the image that exhibit individual texture properties. Existing methods also typically generate a single result, which may not suit the preferences of different users. In this paper, we introduce the Restore Anything Pipeline (RAP), a novel interactive and per-object level image restoration approach that incorporates a controllable model to generate different results that users may choose from. RAP incorporates image segmentation through the recent Segment Anything Model (SAM) into a controllable image restoration model to create a user-friendly pipeline for several image restoration tasks. We demonstrate the versatility of RAP by applying it to three common image restoration tasks: image deblurring, image denoising, and JPEG artifact removal. Our experiments show that RAP produces superior visual results compared to state-of-the-art methods. RAP represents a promising direction for image restoration, providing users with greater control, and enabling image restoration at an object level.

翻译：近期基于深度学习的图像复原方法取得了显著进展。然而，现有方法通常将整张图像视为单一实体，未能考虑图像中具有独特纹理属性的不同物体。此外，现有方法通常仅生成单一结果，可能无法满足不同用户的偏好。本文提出恢复一切管道（RAP），一种新颖的交互式逐物体级图像复原方法，该方法整合可控模型以生成多种结果供用户选择。RAP通过近期推出的分割一切模型（SAM）实现图像分割，并将其融入可控图像复原模型，构建面向多种图像复原任务的用户友好型流水线。我们通过将RAP应用于三项常见图像复原任务（图像去模糊、图像去噪及JPEG伪影去除）来展示其通用性。实验表明，与最先进方法相比，RAP能生成更优的视觉结果。RAP为图像复原开辟了有前景的方向，赋予用户更强的控制能力，并实现了物体级别的图像复原。