Image Super-resolution Based on No Match Generative Adversarial Network

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Image Super-resolution Based on No Match Generative Adversarial Network

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[1]LI Xuexiang,CAO Qi,LIU Chengming.Image Super-resolution Based on No Match Generative Adversarial Network[J].Journal of Zhengzhou University (Engineering Science),2021,42(5):1-6.[doi:10.13705/j.issn.1671-6833.2021.05.018]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42 Number of periods: 2021 Issue 5 Page number: 1-6 Column: Public date: 2021-09-10

Title:: Image Super-resolution Based on No Match Generative Adversarial Network

Author(s):: LI Xuexiang, CAO Qi, LIU Chengming; School of Software, Zhengzhou University, Zhengzhou 450002, China

Keywords:: super-resolution; deep learning; generative adversarial network; no matching; second-order statistic

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.05.018

Abstract:: Image super-resolution reconstruction based on generative adversarial networks (GAN) is subject to the dataset training with an unstable result. To solve this problem, a new NM-SRGAN model is established. The cycle-gan is firstly used as the preprocess module to make the model free from the dataset for training with better input of the image, and the model cancels BN layer to solve the unstable results. Besides, covariance matrix is adopted to capture the second-order information of the image, and second-order loss function is added with a focus on the changes of the image details. The new VGG loss function is used to improve the marginal texture of the image. The proposed NM-SRGAN model is verified by four standard datasets, and the resulting images are assessed by the objective evaluation indices. Compared with the existing models, NM-SRGAN model has an improved evaluation value of 0.19, 0.03, 0.13, and 0.02 dB, respectively, reaching up to the maximum among the four datasets. Results show that the proposed method, compared with traditional algorithms, has achieved better improvements in stability and image quality with better details.

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Last Update: 2021-10-11