Super-resolution image reconstruction network based on dual-channel pixel attention mechanism

Abstract

Abstract In the field of image processing, image super-resolution reconstruction technology based on deep learning is becoming more and more mature. Relying on stacking convolutional layers to expand the depth of the reconstruction network can improve the similarity of the reconstructed image, but as the depth of the convolutional layer continues increase, the learning rate of the reconstruction network will also become lower. In this paper, the dual-channel pixel attention mechanism PA combined with the ESPCN network model can not only retains the high reconstruction rate of ESPCN, but also improves the image reconstruction effect. The experimental results show that the network model proposed in this paper is better than SRCNN, FSRCNN, ESPCN for single image reconstruction, and the peak signal-to-noise ratio (PSNR) and structural similarity ratio (SSIM) are significantly improved.