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

Autor:	Bai, Yunpeng, Wang, Cairong, Xie, Shuzhao, Dong, Chao, Yuan, Chun, Wang, Zhi
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Most existing image restoration methods use neural networks to learn strong image-level priors from huge data to estimate the lost information. However, these works still struggle in cases when images have severe information deficits. Introducing external priors or using reference images to provide information also have limitations in the application domain. In contrast, text input is more readily available and provides information with higher flexibility. In this work, we design an effective framework that allows the user to control the restoration process of degraded images with text descriptions. We use the text-image feature compatibility of the CLIP to alleviate the difficulty of fusing text and image features. Our framework can be used for various image restoration tasks, including image inpainting, image super-resolution, and image colorization. Extensive experiments demonstrate the effectiveness of our method. Comment: 9 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.14736 Zobrazit plný text záznamu View this record from Arxiv