A Comparative Study of Image Denoising Algorithms

Abstract

With the recent advancements in the field of information industry, critical data in the form of digital images is best understood by the human brain. Therefore, digital images play a significant part and backbone role in many areas such as image processing, vision computing, robotics, and bio-medical. Such use of digital images is practically implementable in various real-time scenarios like biological sciences, medicine, gaming technology, computer information and communication technology, data and statistical science, radiological sciences and medical imaging technology, and medical lab technology. However, when any digital image is sent electronically or captured via camera, it is likely to get corrupted or degraded by the available of degradation factors. To eradicate this problem, several image denoising algorithms have been proposed in the literature focusing on robust, low-cost and fast techniques to improve output performance. Consequently, in this research project, an earnest effort has been made to study various image denoising algorithms. A specific focus is given to the start-of-the-art techniques namely: NL-means, K-SVD, and BM3D. The standard images, natural images, texture images, synthetic images, and images from other datasets have been tested via these algorithms, and a detailed set of convincing results have been provided for efficient comparison.