RDASNet: Image Denoising via a Residual Dense Attention Similarity Network

Abstract

In recent years, thanks to the performance advantages of convolutional neural networks (CNNs), CNNs have been widely used in image denoising. However, most of the CNN-based image-denoising models cannot make full use of the redundancy of image data, which limits the expressiveness of the model. We propose a new image-denoising model that aims to extract the local features of the image through CNN and focus on the global information of the image through the attention similarity module (ASM), especially the global similarity details of the image. Furthermore, dilation convolution is used to enlarge the receptive field to better focus on the global features. Moreover, avg-pooling is used to smooth and suppress noise in the ASM to further improve model performance. In addition, through global residual learning, the effect is enhanced from shallow to deep layers. A large number of experiments show that our proposed model has a better image-denoising effect, including quantitative and visual results. It is more suitable for complex blind noise and real images.

Multi-Scale Feature Learning Convolutional Neural Network for Image Denoising

“…The adaptive Wiener filter [ 3 ], the bilateral filter, the Gaussian filter, and the median filter [ 4 ] are some of the more well-known filter-based algorithms. Nevertheless, these algorithms require manual parameter tuning, and there is a risk of losing image details during the denoising process [ 5 , 6 , 7 , 8 , 9 ].…”