SRGAN Optimization Through Enhanced Network Structure and Multi-Loss Functions

An enhanced SRGAN image super-resolution algorithm is proposed to address SRGAN's training instability and limited global information capture. This enhancement optimizes both the network structure and loss function. In network structure optimization, group normalization is introduced as an alternative to batch normalization, addressing its dependency on batch size and improving stability during small-batch training. Additionally, Global Context Block (GCBlock) is integrated into the residual block, enhancing the network's global information capture and detail representation capabilities. In regard to the loss function, this paper introduces L1 loss and MS-SSIM loss as alternatives to the conventional MSE loss. This approach aims to enhance the quality of the generated image in both pixel space and structural information while reducing the smoothing effect. In the experimental section, the ablation experiments evaluate the performance of models with and without GCBlock on five test sets, including U rban1 00 and Manga109. The results demonstrate that GCBlock improves both detail expressiveness and global information capture, with an average PSNR gain of 0.1388 and SSIM improvement of 0.008. In comparison experiments, the improved algorithm achieved an average PSNR increase of 0.270 and an average SSIM increase of 0.018 on the aforementioned test set, indicating a significant improvement in detail expression and visual quality. Finally, the limitations of the enhanced algorithm are discussed, and potential future research directions are proposed.