Distilling Semantic Priors from SAM to Efficient Image Restoration Models

arXiv:2403.16368v1 Announce Type: new
Abstract: In image restoration (IR), leveraging semantic priors from segmentation models has been a common approach to improve performance. The recent segment anything model (SAM) has emerged as a powerful tool for extracting advanced semantic priors to enhance IR tasks. However, the computational cost of SAM is prohibitive for IR, compared to existing smaller IR models. The incorporation of SAM for extracting semantic priors considerably hampers the model inference efficiency. To address this issue, we propose …

abstract advanced arxiv computational cost cs.cv however image image restoration performance sam segment segment anything segment anything model segmentation semantic tasks tool type