Image color rendering based on frequency channel attention GAN

In recent years, channel attention mechanism has greatly improved the performance of computer vision-oriented network models. But the simple superposition of modules inevitably increases the complexity of the model. In order to improve the performance and reduce the complexity of the model, a novel frequency channel attention GAN is proposed and applied to image color rendering. Firstly, global average pooling is a special case of discrete cosine transform. In order to better capture the rich input mode information, we extend global mean pooling to the frequency domain to obtain the frequency channel attention mechanism. Secondly, the frequency channel attention mechanism is combined with U-Net network to represent all the feature information of the image. The effectiveness of channel attention GAN in frequency domain was verified by using DIV2K dataset and COCO dataset. Finally, compared with pix2pix, CycleGAN, and HCEGAN models, PSNR increased by 2.660 dB, 2.595 dB and 1.430 dB, and SSIM increased by 7.943%, 6.790% and 2.436%. Experimental results show that our method not only improves the image rendering effect and quality, but also enhances the model stability.