Research Repository

Multiscale image denoising using goodness-of-fit test based on EDF statistics.

Naveed, Khuram and Shaukat, Bisma and Ehsan, Shoaib and McDonald-Maier, Klaus D and Ur Rehman, Naveed (2019) 'Multiscale image denoising using goodness-of-fit test based on EDF statistics.' PLoS One, 14 (5). e0216197-e0216197. ISSN 1932-6203

Multiscale image denoising using goodness-of-fit test based on EDF statistics.pdf - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview


Two novel image denoising algorithms are proposed which employ goodness of fit (GoF) test at multiple image scales. Proposed methods operate by employing the GoF tests locally on the wavelet coefficients of a noisy image obtained via discrete wavelet transform (DWT) and the dual tree complex wavelet transform (DT-CWT) respectively. We next formulate image denoising as a binary hypothesis testing problem with the null hypothesis indicating the presence of noise and the alternate hypothesis representing the presence of desired signal only. The decision that a given wavelet coefficient corresponds to the null hypothesis or the alternate hypothesis involves the GoF testing based on empirical distribution function (EDF), applied locally on the noisy wavelet coefficients. The performance of the proposed methods is validated by comparing them against the state of the art image denoising methods.

Item Type: Article
Uncontrolled Keywords: Humans; Image Interpretation, Computer-Assisted; Image Enhancement; Artifacts; Statistical Distributions; Algorithms; Computer Simulation; Signal Processing, Computer-Assisted; Wavelet Analysis; Signal-To-Noise Ratio
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 29 Apr 2020 15:00
Last Modified: 15 Jan 2022 01:28

Actions (login required)

View Item View Item