关键词: 生成对抗网络, 图像超分辨率重建, 高频特征, 双判别器, 模式崩溃

Abstract: Due to the instability of network training, the image super-resolution reconstruction based on Generative Adversarial Network (GAN) has a mode collapse phenomenon. To solve this problem, a Spherical double Discriminator Super-Resolution Generative Adversarial Network (SDSRGAN) based on spherical geometric moment matching and feature discrimination was proposed, and the stability of network training was improved by adopting geometric moment matching and discrimination of high-frequency features. First of all, the generator was used to produce a reconstructed image through feature extraction and upsampling. Second, the spherical discriminator was used to map image features to high-dimensional spherical space, so as to make full use of higher-order statistics of feature data. Third, a feature discriminator was added to the traditional discriminator to extract high-frequency features of the image, so as to reconstruct both the characteristic high-frequency component and the structural component. Finally, game training between the generator and double discriminator was carried out to improve the quality of the image reconstructed by the generator. Experimental results show that the proposed algorithm can effectively converge, its network can be stably trained, and has Peak Signal-to-Noise Ratio (PSNR) of 31.28 dB, Structural SIMilarity (SSIM) of 0.872. Compared with Bicubic, Super-Resolution Residual Network (SRResNet), Fast Super-Resolution Convolutional Neural Network (FSRCNN), Super-Resolution using a Generative Adversarial Network (SRGAN), and Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) algorithms, the reconstructed image of the proposed algorithm has more precise structural texture characteristics. The proposed algorithm provides a double discriminant method for spherical moment matching and feature discrimination for the research of image super-resolution based on GAN, which is feasible and effective in practical applications.

Key words: Generative Adversarial Network (GAN), image super-resolution reconstruction, high-frequency feature, double discriminator, mode collapse