Theses and Dissertations
Variational and adaptive non-local image denoising using edge detection and k − means clustering
Mississippi State University
Date of Degree
Graduate Thesis - Open Access
Master of Science (M.S.)
College of Arts and Sciences
Department of Mathematics and Statistics
With the increased presence of image-based data in modern applications, the need for robust methods of image denoising grows greater. The work presented herein considers two of the most ubiquitous approaches towards image denoising: variational and non-local methods. The effectiveness of these methods is assessed using quantitatively using peak signal-to-noise ratio and structural similarity index measure metrics. This study employs ��−means clustering, an unsupervised machine learning algorithm, to isolate the most dominant cluster centroids within the incoming data and propose the introduction of a new adaptive parameter into the non-local means framework. Motivated by the fact that a majority of discrepancies between clean and denoised images occur at feature edges, this study examines several convolution-based edge detection methods to isolate relevant feature. The resultant gradient and edge information is used to further parameterize the ��−means non-local method. An additional hybrid method involving the combined contributions of variational and ��−means non-local denoising is proposed, with the weighting determined by edge intensities. This method outperforms the other methods outlined in the study, both conventional and newly presented.
Mujahid, Shiraz, "Variational and adaptive non-local image denoising using edge detection and k − means clustering" (2023). Theses and Dissertations. 5742.
Available for download on Friday, December 15, 2023