A multi-scale branch convolutional neural network for denoising

Abstract

Images, being significant carriers of memories and information, are valued by people. To restore images, it is necessary to perform noise reduction processing to eliminate noise generated by camera equipment and other factors. Traditional denoising technology such as wavelet transform is used to help engineer restore a image. And in recent years, the introduction of convolutional neural networks has accelerated the progress of noise reduction research. Many classic models have been developed by researchers using U-shaped networks and other techniques. Researchers often use multi-scale approaches to obtain multiple feature maps and enhance their network with these features. Our work enhanced denoising network by introducing large convolutions, small convolutions, and Fast Fourier convolutions to capture feature information at different scales. Additionally, we used an SE block to introduce attention mechanisms into the network. As evidenced by experimental results, our network achieved outstanding performance.