Image denoising using convolutional neural network

Abstract

Satellite and aerial imaging is being one of the most attractive source of information for the governmental agencies and the commercial companies in recent times. The quality of the images is very important to pick the valuable information from the details especially for high value targets. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, vibration and clouds. There are several image denoising algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Many of the traditional denoising methods can filter noise, but at the same time they make the image details fuzzy. This paper presents the convolutional neural network (CNN) based image denoising method that retains the image detail but removes the unwanted noise. The proposed method employs a residual learning strategy, meaning that the CNN network learns to estimate the residual image. A residual image is the difference between a pristine image and a distorted copy of the image, and contains information about the image distortion. An extensive experiments demonstrate that proposed CNN denoising model can not only exhibit high effectiveness in image denoising tasks, but also be efficiently implemented by benefiting from GPU computing. The proposed method is applied to both aerial and satellite imagery and effectiveness is measured using Peak Signal to Noise Ratio (PSNR), Structure Similarity Index Metric (SSIM), and Naturalness Image Quality Evaluator (NIQE), which is also called perceptual quality index.