Flexible Image Denoising with Multi-layer Conditional Feature Modulation

• URL: http://arxiv.org/abs/2006.13500v1
• Date: Wed, 24 Jun 2020 06:00:00 GMT
• Title: Flexible Image Denoising with Multi-layer Conditional Feature Modulation
• Authors: Jiazhi Du, Xin Qiao, Zifei Yan, Hongzhi Zhang, and Wangmeng Zuo
• Abstract summary: We present a novel flexible image enoising network (CFMNet) by equipping an U-Net backbone with conditional feature modulation (CFM) modules. In comparison to channel-wise shifting only in the first layer, CFMNet can make better use of noise level information by deploying multiple layers of CFM. Our CFMNet is effective in exploiting noise level information for flexible non-blind denoising, and performs favorably against the existing deep image denoising methods in terms of both quantitative metrics and visual quality.
• Score: 56.018132592622706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model. However, in this kind of solution, the noise variance (i.e., noise level) is only deployed to modulate the first layer of convolution feature with channel-wise shifting, which is limited in balancing noise removal and detail preservation. In this paper, we present a novel flexible image enoising network (CFMNet) by equipping an U-Net backbone with multi-layer conditional feature modulation (CFM) modules. In comparison to channel-wise shifting only in the first layer, CFMNet can make better use of noise level information by deploying multiple layers of CFM. Moreover, each CFM module takes onvolutional features from both noisy image and noise level map as input for better trade-off between noise removal and detail preservation. Experimental results show that our CFMNet is effective in exploiting noise level information for flexible non-blind denoising, and performs favorably against the existing deep image denoising methods in terms of both quantitative metrics and visual quality.

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