A novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications

Abstract

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.