TextIR: A Simple Framework for Text-Based Editable Image Restoration

Abstract

Many current image restoration approaches utilize neural networks to acquire robust image-level priors from extensive datasets, aiming to reconstruct missing details. Nevertheless, these methods often falter with images that exhibit significant information gaps. While incorporating external priors or leveraging reference images can provide supplemental information, these strategies are limited in their practical scope. Alternatively, textual inputs offer greater accessibility and adaptability. In this study, we develop a sophisticated framework enabling users to guide the restoration of deteriorated images via textual descriptions. Utilizing the text-image compatibility feature of CLIP enhances the integration of textual and visual data. Our versatile framework supports multiple restoration activities such as image inpainting, super-resolution, and colorization. Comprehensive testing validates our technique's efficacy.