Image Super-Resolution Reconstruction Algorithm Based on SRGAN and Swin Transformer

Existing methods have problems such as loss of details and insufficient reconstruction effect when processing complex images. To improve the quality and efficiency of image super-resolution reconstruction, this study proposes an improved algorithm based on super-resolution generative adversarial network and Swin Transformer. Firstly, on the ground of the traditional super-resolution generative adversarial network, combined with the global feature extraction capability of Swin Transformer, the model’s capacity to capture multi-scale features and restore details is enhanced. Subsequently, by utilizing adversarial loss and perceptual loss to further optimize the training process, the image’s visual quality is improved. The results show that the optimization algorithm had high PSNR and structural similarity index values in multiple benchmark test datasets, with the highest reaching 43.81 and 0.94, respectively, which are significantly better than the comparison algorithm. In practical applications, this algorithm demonstrated higher reconstruction accuracy and efficiency when reconstructing images with complex textures and rich edge details. The highest reconstruction accuracy could reach 98.03%, and the reconstruction time was as low as 0.2 s or less. In summary, this model can greatly improve the visual quality of image super-resolution reconstruction, better restore details, reduce detail loss, and provide an efficient and reliable solution for image super-resolution reconstruction tasks.