Image super resolution reconstruction algorithm based on generative countermeasure network

SRGAN is a typical method of image super-resolution based on deep learning, the reconstruction effect is good, but the algorithm still has some shortcomings, and there is still more room for improving the image quality and operation speed. An optimization model is proposed based on the SRGAN network model. Because the batch normalization (BN) layer often ignores some image details in super-resolution image reconstruction and increases the complexity of the network at the same time, the BN layer is removed from the generator of SRGAN and the ECA channel attention is introduced so that each residual block generating feature map gets a corresponding weight in order to process more image details. After training and comparison experiments on public datasets, the results show that the proposed improved model has richer image details recovery, better visual effects, better peak signal-to-noise ratio and structural similarity performance, and fewer total number of model parameters compared to the comparison model.