Mean curvature regularization-based Poisson image restoration.

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Bibliographic Details
Title: Mean curvature regularization-based Poisson image restoration.
Authors: Fuquan Ren1 petterren@gmail.com, Tianshuang Qiu1, Hui Liu1
Source: Journal of Electronic Imaging. May/Jun2015, Vol. 24 Issue 3, p1-15. 15p.
Subjects: Poisson processes, Image reconstruction, Image processing, Lagrange multiplier, Mathematical optimization
Abstract: The restoration of blurred images corrupted by Poisson noise is an important task in various applications such as medical imaging, microscopy imaging, and so on. We focus on mean curvature-based regularization to address the Poisson noise image restoration problem. Furthermore, we derive a numerical algorithm based on the augmented Lagrange multiplier method with a splitting technique. In order to simultaneously demonstrate the effectiveness of the proposed method for Poisson noise removal with deblurring, we conduct systematic experiments on both nature images and biological images. Experimental results show that the proposed approach can produce higher quality results and more natural images compared to some state-of-the-art variational algorithms recently developed. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The restoration of blurred images corrupted by Poisson noise is an important task in various applications such as medical imaging, microscopy imaging, and so on. We focus on mean curvature-based regularization to address the Poisson noise image restoration problem. Furthermore, we derive a numerical algorithm based on the augmented Lagrange multiplier method with a splitting technique. In order to simultaneously demonstrate the effectiveness of the proposed method for Poisson noise removal with deblurring, we conduct systematic experiments on both nature images and biological images. Experimental results show that the proposed approach can produce higher quality results and more natural images compared to some state-of-the-art variational algorithms recently developed. [ABSTRACT FROM AUTHOR]
ISSN:10179909
DOI:10.1117/1.JEI.24.3.033025