Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

图像去噪面临着来自噪声源的重大挑战。具体来说，高斯噪声、脉冲噪声、盐噪声、胡椒噪声和斑点噪声是成像中复杂的噪声源。卷积神经网络（CNN）在图像去噪任务中越来越受到关注。已经研究了几种用于去噪图像的CNN方法。这些方法使用不同的数据集进行评估。在本文中，我们详细研究了用于图像去噪的不同 CNN 技术。对不同的 CNN 图像去噪方法进行了分类和分析。研究了用于评估 CNN 图像去噪方法的流行数据集。选择了几篇CNN图像去噪论文进行审查和分析。概述了 CNN 方法的动机和原理。一些最先进的 CNN 图像去噪方法以图形形式进行了描述，而其他方法则进行了详细解释。我们提出了对 CNN 图像去噪的回顾。选择了以前和最近关于 CNN 图像去噪的论文。未来研究的潜在挑战和方向也同样得到了充分说明。