Multi-scale frequency separation network for image deblurring

• URL: http://arxiv.org/abs/2206.00798v1
• Date: Wed, 1 Jun 2022 23:48:35 GMT
• Title: Multi-scale frequency separation network for image deblurring
• Authors: Yanni Zhang, Qiang Li, Miao Qi, Di Liu, Jun Kong, Jianzhong Wang
• Abstract summary: We present a new method called multi-scale frequency separation network (MSFS-Net) for image deblurring. MSFS-Net captures the low and high-frequency information of image at multiple scales. Experiments on benchmark datasets show that the proposed network achieves state-of-the-art performance.
• Score: 10.511076996096117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image deblurring aims to restore the detailed texture information or structures from the blurry images, which has become an indispensable step in many computer-vision tasks. Although various methods have been proposed to deal with the image deblurring problem, most of them treated the blurry image as a whole and neglected the characteristics of different image frequencies. In this paper, we present a new method called multi-scale frequency separation network (MSFS-Net) for image deblurring. MSFS-Net introduces the frequency separation module (FSM) into an encoder-decoder network architecture to capture the low and high-frequency information of image at multiple scales. Then, a simple cycle-consistency strategy and a sophisticated contrastive learning module (CLM) are respectively designed to retain the low-frequency information and recover the high-frequency information during deblurring. At last, the features of different scales are fused by a cross-scale feature fusion module (CSFFM). Extensive experiments on benchmark datasets show that the proposed network achieves state-of-the-art performance.

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