Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network

Aiming at the problem that the existing pixel loss-based super-resolution image reconstruction algorithms have poor reconstruction effect on high-frequency details, such as textures, an image reconstruction algorithm based on an improved super-resolution generative adversarial network (SRGAN) is proposed in this paper. First, remove the batch normalization layers in the generator, combine the multi-level residual network and dense connections, and use the residual-in-residual dense blocks to improve the network's ability for feature extraction. Then, the mean square error and perceptual loss are combined as the loss function to guide the generator training, which preserves the image's high-frequency details and avoids the artifacts' appearance. Finally, the last Sigmoid layer of the discriminator is removed to better converge the training process, and the relativistic loss function is used to guide the discriminator training. The experimental results on the COCO dataset show that compared with the original SRGAN algorithm, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the algorithm in the Set5 data set are increased by 0.86 dB and 0.0123, respectively, in the Set14 data set, the PSNR and SSIM of the algorithm are improved by 0.69 dB and 0.0090, respectively. The mean opinion index and visual effect of the algorithm are far better than other algorithms.