Mr. Chen Feng Profile

Chen Feng

at Nanjing Univ of Science and Technology

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Proceedings Article | 19 December 2013 Paper

Application of wavelet semi-soft threshold filter algorithm in EMCCD's image processing

Feng Chen, Wenwen Zhang, Qian Chen, Guohua Gu

Proceedings Volume 9045, 904512 (2013) https://doi.org/10.1117/12.2034301

KEYWORDS: Image filtering, Electron multiplying charge coupled devices, Image processing, Electronic filtering, Digital filtering, Wavelets, Expectation maximization algorithms, Denoising, Interference (communication), Nonlinear filtering

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Electron Multiplication Charge Couple Device (EMCCD) has an outstanding performance in the low-light imaging field for its high sensitivity, high quantum efficiency, and low noise characteristics. Generally we obtain clear low-light images by increasing the multiplication gain of EMCCD. However, with the gain improved the noise will increase rapidly at the same moment, which makes a big influence on EMCCD imaging quality. At present the noise parameter estimation algorithms of EMCCD mainly have maximum likelihood estimation method and expectation maximization estimation method, etc. These algorithms are complicated and the requirement for initial value is high which make them more difficult to achieve. On the other hand, the moment estimation method applied in this paper has a lower complexity and a wider application. So in this paper we have made a study of the particularity and complexity of EMCCD noise distribution model and then established a suitable noise distribution model for image processing. We calculated the EMCCD noise parameter estimation by using the moment estimation method, and obtained a higher accuracy of noise parameter estimates. Then we used the wavelet semi-soft threshold algorithm into EMCCD image noise filtering processing while the image was added the mixed Poisson-Gaussian noise generated by the simulation of moment estimation. At the end, the simulation results show that the algorithm we used can filter out noise effectively, restore clear images, and can retain details and edge information of image at the same time.

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