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Sensors 2017, 17(2), 233;

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 17 November 2016 / Revised: 22 December 2016 / Accepted: 9 January 2017 / Published: 26 January 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [18936 KB, uploaded 26 January 2017]   |  


An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image. View Full-Text
Keywords: ICCD image sensor; low-light-level; sparse representation; randomly clustered noise; image denoising ICCD image sensor; low-light-level; sparse representation; randomly clustered noise; image denoising

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wang, F.; Wang, Y.; Yang, M.; Zhang, X.; Zheng, N. A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image. Sensors 2017, 17, 233.

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