Abstract
Many image systems rely on photon detection as a basis of image formation. One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process. Unlike additive Gaussian noise, Poisson noise is signal dependent, and consequently separating signal from noise is a very difficult task. In most current Poisson noise reduction algorithms, noisy signal is firstly pre-processed to approximate Gaussian noise and then denoise by a conventional Gaussian denoising algorithm. In this paper, based on the property that Poisson noise adapts to the intensity of signal, we develop and analyze a new method using an optimal ICA-domain filter for Poisson noise removal. The performance of this algorithm is assessed with simulated data experiments and experimental results demonstrate that this algorithm greatly improves the performance in denoising image.
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© 2003 Springer-Verlag Berlin Heidelberg
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Han, XH., Chen, YW., Nakao, Z. (2003). An ICA-Based Method for Poisson Noise Reduction. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_195
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DOI: https://doi.org/10.1007/978-3-540-45224-9_195
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
Online ISBN: 978-3-540-45224-9
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