Improving Image Restoration through Removing Degradations in Textual Representations

In this paper we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively restoration is much easier on text modality than image one. For example it can be easily conducted by removing degradation-related words while keeping the content-aware words. Hence we combine the advantages of images in detail description and ones of text in degradation removal to perform restoration. To address the cross-modal assistance we propose to map the degraded images into textual representations for removing the degradations and then convert the restored textual representations into a guidance image for assisting image restoration. In particular We ingeniously embed an image-to-text mapper and text restoration module into CLIP-equipped text-to-image models to generate the guidance. Then we adopt a simple coarse-to-fine approach to dynamically inject multi-scale information from guidance to image restoration networks. Extensive experiments are conducted on various image restoration tasks including deblurring dehazing deraining and denoising and all-in-one image restoration. The results showcase that our method outperforms state-of-the-art ones across all these tasks. The codes and models are available at https://github.com/mrluin/TextualDegRemoval.