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

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