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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2019-01, Vol.19 (1), p.206
• Main Authors: Naveed, Khuram, Ehsan, Shoaib, McDonald-Maier, Klaus D, Ur Rehman, Naveed
• Format: Article
• Language: English
• Subjects: Architectural engineering; binary hypothesis testing; CMOS; CMOS/CCD image sensors; Computer engineering; detection theory; Distribution functions; Gaussian and Poisson denoising; Hypotheses; Hypothesis testing; Image acquisition; Image detection; International conferences; multiscale; Noise; Noise reduction; Normal distribution; Principal components analysis; Random noise; Sensors; Signal processing; Theory; variance stability transformation (VST); Variations; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19010206

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.