Image Denoising using Deep Learning: Convolutional Neural Network

It has become an important task to remove noise from the image and restore a high-quality image in order to process image further for the purpose like object segmentation, detection, tracking etc. This paper presents denoising of image using the convolutional neural network (CNN) model in deep learning. This analysis is done by adding 1% to 10% Gaussian white noise to the image and then applying CNN model to denoise it. Further, qualitative and quantitative analysis of the denoised image is performed. Under qualitative analysis comes the quality of image where edge factor, texture, uniform region and non-uniform region, smoothness, structure of objects is considered. The quantitative analysis is done using the three metrics which are PSNR (peak signal to noise ratio), SSIM (structural similarity index measurement), and MSE (mean square error) in which the CNN based method's results are compared with the traditional or standard methods of image denoising. The results from the analysis and experiment show that the CNN model can efficiently remove a lot of Gaussian noise and restore the image details and data than any other traditional/standard image filtering techniques.