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A Multiscale Denoising Framework using Detection Theory with Application to Images from CMOS/CCD Sensors

Naveed, Khuram and Ehsan, Shoaib and McDonald-Maier, Klaus and ur Rehman, Naveed (2019) 'A Multiscale Denoising Framework using Detection Theory with Application to Images from CMOS/CCD Sensors.' Sensors, 19 (1). p. 206. ISSN 1424-2818

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Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory of hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson-Gaussian denoising. VST transforms signal dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subsequently, multiscale transforms are employed on the noisy image to segregate signal and noise into separate coefficients. That facilitates the application of local binary hypothesis testing on multiple scales using empirical distribution function (EDF) for the purpose of detection and removal of noise. We demonstrate the effectiveness of the proposed framework with different multiscale transforms and on a wide variety of input datasets.

Item Type: Article
Uncontrolled Keywords: Multiscale; Gaussian and Poisson denoising; CMOS/CCD Image Sensors; Detection 12 theory; Binary hypothesis testing; Variance stability transformation (VST)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
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: 08 Apr 2019 13:26
Last Modified: 15 Jan 2022 01:26

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