Abstract: An image super-resolution reconstruction model combining coordinate attention and generative adversarial networks is proposed to address the problems of inadequate utilization of feature information, weak judgment of local details by VGG discriminators, and unstable training in the existing image super-resolution reconstruction model of generative adversarial networks. Firstly, a generator is constructed with residual blocks embedded with coordinate attention to aggregate features along both channel and spatial dimensions to extract features more adequately. The Dropout is also adjusted to join the network in such a way that it acts in the generator to improve the generalization ability of the model. Secondly, the discriminator is constructed with U-Net structure to output detailed pixel-by-pixel feedback to obtain the local difference between the true and false images. Finally, spectral normalization regularization is introduced into the discriminator to stabilize the training of GAN. The experimental results show that when the amplification factor is 4, the peak signal-to-noise ratio obtained on the benchmark test sets Set5 and Set14 is increased by 1.75 dB on average, and the structural similarity is increased by 0.038 on average, which can reconstruct clearer and more realistic images with good visual effects.

Key words: super-resolution reconstruction, generative adversarial network, coordinate attention, U-Net discriminator