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Most existing image restoration methods use neural networks to learn strongimage-level priors from huge data to estimate the lost information. However,these works still struggle in cases when images have severe informationdeficits. Introducing external priors or using reference images to provideinformation also have limitations in the application domain. In contrast, textinput is more readily available and provides information with higherflexibility. In this work, we design an effective framework that allows theuser to control the restoration process of degraded images with textdescriptions. We use the text-image feature compatibility of the CLIP toalleviate the difficulty of fusing text and image features. Our framework canbe used for various image restoration tasks, including image inpainting, imagesuper-resolution, and image colorization. Extensive experiments demonstrate theeffectiveness of our method.

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