A multiscale denoising framework using detection theory with application to images from CMOS/CCD sensors

TY - JOUR

T1 - A multiscale denoising framework using detection theory with application to images from CMOS/CCD sensors

AU - Naveed, Khuram

AU - Ehsan, Shoaib

AU - McDonald-Maier, Klaus D.

AU - Ur Rehman, Naveed

N1 - Funding Information: This work is supported by the UK EPSRC through grants EP/P017487/1 and EP/R02572X/1. Publisher Copyright: © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 and 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.

AB - 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 and 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.

KW - Binary hypothesis testing

KW - CMOS/CCD image sensors

KW - Detection theory

KW - Gaussian and Poisson denoising

KW - Multiscale

KW - Variance stability transformation (VST)

UR - http://www.scopus.com/inward/record.url?scp=85059797862&partnerID=8YFLogxK

U2 - 10.3390/s19010206

DO - 10.3390/s19010206

M3 - Journal article

C2 - 30626102

AN - SCOPUS:85059797862

SN - 1424-8220

VL - 19

JO - Sensors (Switzerland)

JF - Sensors (Switzerland)

IS - 1

M1 - 206

ER -