Single Image Super-Resolution: Depthwise Separable Convolution Super-Resolution Generative Adversarial Network

Jiang, ZT; Huang, YS; Hu, LR

Huang, YS (corresponding author), Guilin Univ Elect Technol Univ, Guangxi Key Lab Image & Graph Intelligent Proc, Guilin 541004, Peoples R China.

APPLIED SCIENCES-BASEL, 2020; 10 (1):

Abstract

The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image sup......

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