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Image Super-Resolution Reconstruction Using Generative Adversarial Networks Based on Wide-Channel Activation

文献类型： 外文期刊

作者： Sun, Xudong 1 ; Zhao, Zhenxi 1 ; Zhang, Song 2 ; Liu, Jintao 2 ; Yang, Xinting 2 ; Zhou, Chao 2 ;

作者机构： 1.East China Jiaotong Univ, Sch Mechatron Engn, Nanchang 330013, Jiangxi, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China

5.Univ Almeria, Sch Engn, Almeria 04120, Spain

关键词： Super-resolution; residual block; relativistic average discriminator; generative adversarial network; perceived quality

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2020 年 8 卷

页码：

收录情况： SCI

摘要： In recent years, residual learning has shown excellent performance on convolutional neural network (CNN)-based single-image super-resolution (SISR) tasks. However, CNN-based SISR approaches have focused mainly on the design of deep architectures, and the rectified linear units (ReLUs) used in these networks hinder shallow-to-deep information transfer. As a result, these methods are unable to utilize some shallow information, and improving model performance is difficult. To solve the above issues, this paper proposes an image SR reconstruction method based on a generative adversarial network with a residual dense architecture. First, before ReLU activation, the number of feature channels is expanded by a factor of 6 similar to 9 using a 1 x 1 convolutional layer, which improves the utilization of shallow information. Next, the original discriminator is replaced with a relativistic average discriminator, thereby improving the authenticity of the discriminative network. Finally, preactivation features are used to improve the perceptual loss, thus providing stronger monitoring for brightness consistency and texture restoration. Experimental results show that the proposed algorithm improves the utilization of shallow information in a deep network. Structural similarity (SSIM) index evaluations show that the overall utilization of shallow information is increased by 105.52%. In addition, the average runtime is 0.42 sec/frame, nearly 3.6 times faster than those of traditional methods. Moreover, the recovered images have an average natural image quality evaluator value of 3.4 and high perceptual quality, showing that the proposed method is suitable for image reconstruction applications in fields such as agriculture and medicine.

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