Image Super-Resolution Reconstruction Based on Enhanced Attention Mechanism and Gradient Correlation Loss

In the field of super-resolution reconstruction, generative adversarial networks are able to generate textures that are more in line with the perception of the human eyes, but low-resolution images often encounter information loss and edge blurring problems in the process of reconstruction. In order to solve this problem, this article proposed an image super-resolution reconstruction model based on an enhanced attention mechanism and gradient correlation loss, which can better focus on important details in low-resolution images, thus improving the quality of reconstructed images. Firstly, an enhanced attention mechanism is proposed and incorporated into the generator model as a way to reduce the amount of information loss during image feature extraction and retain more image details. Furthermore, this paper proposed a gradient correlation loss function to maximize the correlation between the gradient of the generated image and the gradient of the original image. Thus, the generated image is more realistic and maintains a consistent edge structure. Finally, the experimental results on the standard dataset show that compared with other representative algorithms, the proposed algorithm has achieved some improvement in PSNR, SSIM, and LPIPS, which can verify the effectiveness of the algorithm.