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

ABSTRACT: 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

SUBMITTER: Naveed K 

PROVIDER: S-EPMC6338905 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature