LIRA: Lifelong Image Restoration from Unknown Blended Distortions

• URL: http://arxiv.org/abs/2008.08242v1
• Date: Wed, 19 Aug 2020 03:35:45 GMT
• Title: LIRA: Lifelong Image Restoration from Unknown Blended Distortions
• Authors: Jianzhao Liu, Jianxin Lin, Xin Li, Wei Zhou, Sen Liu, Zhibo Chen
• Abstract summary: We propose a 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. We develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge.
• Score: 33.91806781681914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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