Residual Dilated Network with Attention for Image Blind Denoising

Image denoising has recently witnessed substantial progress. However, many existing methods remain suboptimal for texture restoration due to treating different image regions and channels indiscriminately. Also they need to specify the noise level in advance, which largely hinders their use in blind denoising. Therefore, we introduce both attention mechanism and automatic noise level estimation into image denoising. Specifically, we propose a new, effective end-toend attention-embedded neural network for image denoising, named as Residual Dilated Attention Network (RDAN). Our RDAN is composed of a series of tailored Residual Dilated Attention Blocks (RDAB) and Residual Conv Attention Blocks (RCAB). The RDAB and RCAB incorporates both non-local and local operations, which enable a comprehensive capture of structural information. In addition, we incorporate a Gaussian-based noise level estimation into RDAN to accomplish blind denoising. Experimental results have demonstrated that our RDAN can substantially outperforms the state-of-the-art denoising methods as well as promisingly preserve image texture

DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet