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修复方法取得了显著进展。然而，现有方法往往将整幅图像视为单一实体，未能考虑图像中具有各自纹理特性的不同物体。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。本文提出修复万物流水线（RAP），这是一种新颖的交互式逐物体级别图像修复方法，通过融合可控模型生成多种结果供用户选择。RAP将近期提出的分割万物模型（SAM）实现的图像分割技术集成到可控图像修复模型中，构建了一个适用于多种图像修复任务的用户友好型流水线。我们通过将RAP应用于图像去模糊、图像去噪和JPEG伪影去除三项常见图像修复任务，验证了其通用性。实验表明，与最先进方法相比，RAP能够生成更优的视觉结果。RAP为图像修复领域开辟了富有前景的新方向，赋予用户更强的控制能力，并实现了物体级别的图像修复。