Restore Anything Pipeline: Segment Anything Meets Image Restoration

• URL: http://arxiv.org/abs/2305.13093v2
• Date: Sun, 2 Jul 2023 13:42:46 GMT
• Title: Restore Anything Pipeline: Segment Anything Meets Image Restoration
• Authors: Jiaxi Jiang, Christian Holz
• Abstract summary: We introduce the Restore Anything Pipeline (RAP), a novel interactive and per-object level image restoration approach. RAP incorporates image segmentation through the recent Segment Anything Model (SAM) into a controllable image restoration model. RAP produces superior visual results compared to state-of-the-art methods.
• Score: 27.93942383342829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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