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http://dx.doi.org/10.15701/kcgs.2019.25.3.1

Image Restoration Network with Adaptive Channel Attention Modules for Combined Distortions  

Lee, Haeyun (Department of Information and Communication Engineering, DGIST)
Cho, Sunghyun (Department of Information and Communication Engineering, DGIST)
Publication Information
Journal of the Korea Computer Graphics Society / v.25, no.3, 2019 , pp. 1-9 More about this Journal
Abstract
The image obtained from systems such as autonomous driving cars or fire-fighting robots often suffer from several degradation such as noise, motion blur, and compression artifact due to multiple factor. It is difficult to apply image recognition to these degraded images, then the image restoration is essential. However, these systems cannot recognize what kind of degradation and thus there are difficulty restoring the images. In this paper, we propose the deep neural network, which restore natural images from images degraded in several ways such as noise, blur and JPEG compression in situations where the distortion applied to images is not recognized. We adopt the channel attention modules and skip connections in the proposed method, which makes the network focus on valuable information to image restoration. The proposed method is simpler to train than other methods, and experimental results show that the proposed method outperforms existing state-of-the-art methods.
Keywords
Image restoration; Deep learning; Channel attention; CNN;
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