NDLI: An effective example-based learning method for denoising of medical images corrupted by heavy Gaussian noise and poisson noise

Content Provider	IEEE Xplore Digital Library
Author	Trinh, Dinh-Hoan Luong, Marie Dibos, Franccoise Rocchisani, Jean-Marie Pham, Canh-Duong Linh-Trung, Nguyen Nguyen, Truong Q.
Copyright Year	2014
Description	Author affiliation: L2TI, Université Paris 13, 99 Avenue Jean-Baptiste Clément, Villetaneuse, France (Luong, Marie) \|\| Center for Informatics and Computing, VAST, Vietnam (Trinh, Dinh-Hoan; Pham, Canh-Duong) \|\| Fac. Electronics & Telecom., University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam (Linh-Trung, Nguyen) \|\| LAGA, Université Paris 13, 99 Avenue Jean-Baptiste Cle´ment, Villetaneuse, France (Dibos, Franccoise; Rocchisani, Jean-Marie) \|\| Dept. Electrical & Computer Engineering, University of California, San Diego, USA (Nguyen, Truong Q.)
Abstract	Denoising is an essential application to improve image quality, especially in medical imaging. This paper introduces an example and patch-based learning method for reducing Gaussian noise and Poisson noise which often appear in medical imaging modalities using ionizing radiation. In the proposed method, denoising is performed by learning the regression model based on a set of the nearest neighbors of a given noisy patch, with the help of a given set of standard images. The method is evaluated and compared to several state-of-the-art denoising methods. The obtained results confirm its efficiency, especially for heavy noise.
Starting Page	823
Ending Page	827
File Size	884626
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025165
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Noise reduction Training Biomedical imaging Noise measurement Computed tomography Standards Poisson noise Denoising Learning Medical imaging Gaussian noise
Content Type	Text
Resource Type	Article

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