Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.
@article{arxiv.2008.08242,
title = {LIRA: Lifelong Image Restoration from Unknown Blended Distortions},
author = {Jianzhao Liu and Jianxin Lin and Xin Li and Wei Zhou and Sen Liu and Zhibo Chen},
journal= {arXiv preprint arXiv:2008.08242},
year = {2020}
}