YING Changsheng, ZHAO Peng, YUE Dan, LI Ye. Low Light Level Image Enhancement Based on Multi-layer Slicing Photon Localization Algorithm[J]. Chinese Journal of Electronics, 2018, 27(3): 521-526. doi: 10.1049/cje.2018.02.020
Citation: YING Changsheng, ZHAO Peng, YUE Dan, LI Ye. Low Light Level Image Enhancement Based on Multi-layer Slicing Photon Localization Algorithm[J]. Chinese Journal of Electronics, 2018, 27(3): 521-526. doi: 10.1049/cje.2018.02.020

Low Light Level Image Enhancement Based on Multi-layer Slicing Photon Localization Algorithm

doi: 10.1049/cje.2018.02.020
Funds:  This work is supported by the "111" Project of China (No.D17017) and the Key Laboratories Foundation Program (No.BJ2014009).
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  • Corresponding author: LI Ye (corresponding author) was born in Jilin Province, China. He received the Ph.D. degree in electronic science and technology from Changchun University of Science and Technology, China. He is a professor of the School of Science, Changchun University of Science and Technology. His research interest is photoelectric imaging devices and systems. (Email:liyecust@163.com)
  • Received Date: 2017-08-21
  • Rev Recd Date: 2017-12-10
  • Publish Date: 2018-05-10
  • Low light level (LLL) images, which were captured by Intensified CCD (ICCD) camera equipped with an image intensifier, suffer low spatial resolution and contrast due to noise and dispersion. By dividing the integration time into intervals short enough, we obtain photon images where photon formed spots were nearly nonoverlapping. In order to enhance LLL images, we propose a Multi-layer slicing (MLS) photon localization algorithm based on photon images. The photon image is sliced by different planes. Photon spatial distribution (PSD) information is acquired by using projected area ratio, circularity and the number of slices. The enhanced LLL image is obtained by accumulating time-domain correlated PSD images. Experimental results show that the visual effects, spatial frequencies and contrast of the enhanced image are significantly improved.
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