Deep unsupervised learning for image super-resolution with generative adversarial network
作者：
Highlights：
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• A new Super-Resolution model to solve ill-posed problem with unsupervised learning.
• A regularizer for smoothing continual section of image and preserving sharp edge.
• A loss function for training the proposed SR model with end-to-end learning manner.
• Detailed study for regular items and downsampling strategies.
• Comparable SR performance with faster inference speed.
摘要
•A new Super-Resolution model to solve ill-posed problem with unsupervised learning.•A regularizer for smoothing continual section of image and preserving sharp edge.•A loss function for training the proposed SR model with end-to-end learning manner.•Detailed study for regular items and downsampling strategies.•Comparable SR performance with faster inference speed.
论文关键词：Super-resolution,Deep unsupervised learning,Sub-pixel convolution,Regularizer,Generative adversarial network
论文评审过程：Received 22 April 2018, Revised 3 July 2018, Accepted 4 July 2018, Available online 25 July 2018, Version of Record 25 July 2018.
论文官网地址：https://doi.org/10.1016/j.image.2018.07.003
