The elimination of noisy content from digital images is one of the major issues during image pre-processing. The process of image acquisition, compression, and image transmission is a major reason for image noise that causes loss of information. This loss of information causes irregularities and error in the working of many real-time applications such as computerized photography, hurdle detection and traffic monitoring (computer vision), automatic character recognition, morphing, and surveillance applications. This paper proposes a new hybrid and multi-level digital image denoising approach (MLAC) using a convolutional neural network (CNN) and anisotropic diffusion (AD). The denoising approach uses a hybrid combination of CNN and AD using multi-level implementation. First of all, CNN is applied to noisy images for noise elimination, which results in a denoised image in the first level of image denoising. After that, denoised image is passed to AD in the second level of image denoising. The AD is applied for edge and corner preservation of objects. This hybrid approach is highly efficient in removing noise while preserving fine details of image. The proposed denoising method is experimented on all standard inbuilt image datasets of Matlab framework. It is tested on SAR images as well. The results are compared with those of some of the latest works in the field of CNN and AD. The quality of the denoised image is tested by using naked eye visual analysis factors and quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), universal image quality index (UIQI), feature similarity index metric (FSIM), equivalent numbers of looks (ENL), noise variance (NV), and mean-squared error (MSE). The denoising results are further critically analyzed using zooming analysis method, plotting histogram, comparative running real-time implementation aspects, and time complexity evaluation. The detailed study of result confirms that the proposed approach gives an excellent result in terms of structure, edge preservation, and noise suppression.

消除数字图像中的嘈杂内容是图像预处理期间的主要问题之一。图像获取，压缩和图像传输的过程是图像噪声导致信息丢失的主要原因。信息的丢失在许多实时应用程序的工作中引起不规范和错误，例如计算机摄影，跨栏检测和交通监控（计算机视觉），自动字符识别，变形和监视应用程序。本文提出了一种使用卷积神经网络（CNN）和各向异性扩散（AD）的新型混合多级数字图像去噪方法（MLAC）。去噪方法使用CNN和AD的混合组合，并使用多级实现。首先，CNN应用于嘈杂的图像以消除噪声，在第一级图像去噪中得到去噪的图像。之后，在第二级图像去噪中，将去噪图像传递给AD。AD用于对象的边缘和角落保存。这种混合方法在去除噪声的同时保持图像的精细细节非常有效。所提出的去噪方法在Matlab框架的所有标准内置图像数据集上进行了实验。它还在SAR图像上进行了测试。将结果与CNN和AD领域的一些最新著作进行了比较。通过使用肉眼视觉分析因素和定量指标（例如峰信噪比（PSNR），结构相似性指标度量（SSIM），通用图像质量指标（UIQI），特征相似性指标）来测试去噪图像的质量指标（FSIM），等效外观（ENL），噪声方差（NV）和均方误差（MSE）。使用缩放分析方法，绘制直方图，比较运行实时实现方面以及时间复杂度评估，进一步对降噪结果进行严格分析。对结果的详细研究证实，该方法在结构，边缘保留和噪声抑制方面均提供了出色的结果。