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 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.
Highlights
Digital images acquired using complementary metal oxide semiconductors (CMOS) or charged coupled devices (CCD) image sensors are subject to noise from two notable sources, i.e., electronic instruments and the photo-sensing devices [1,2]
Poisson and mixed Poisson–Gaussian noise from CMOS/CCD sensors; (ii) the proposed framework is generalized in a sense that it has been implemented by using linear and non-linear multiscale transform domain methods; (iii) complete theoretical and mathematical framework of the proposed methodology is presented in the context of detection theory; (iv) extensive results on images corrupted with non-Gaussian noise have been included with special emphasis on images obtained from
We report quantitative denoising performance using the peak signal to noise ratio (PSNR), where each reported PSNR value is the average of J = 20 iterations
Summary
Digital images acquired using complementary metal oxide semiconductors (CMOS) or charged coupled devices (CCD) image sensors are subject to noise from two notable sources, i.e., electronic instruments and the photo-sensing devices [1,2]. An alternate approach to the problem involves ‘Gaussianization’ of Poisson noise through variance stability transformation (VST), followed by traditional noise filtering methods [19] In this class of methods, first, VST is applied on a low (photon) count image, resulting in transformation of Poisson noise to approximately Gaussian noise which is signal independent and has constant variance. Poisson and mixed Poisson–Gaussian noise from CMOS/CCD sensors; (ii) the proposed framework is generalized in a sense that it has been implemented by using linear and non-linear multiscale transform domain methods; (iii) complete theoretical and mathematical framework of the proposed methodology is presented in the context of detection theory; (iv) extensive results on images corrupted with non-Gaussian noise have been included with special emphasis on images obtained from.
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