• Chinese Optics Letters
  • Vol. 19, Issue 4, 041102 (2021)
Junhao Gu1、2, Shuai Sun1、2, Yaokun Xu1、2, Huizu Lin1、2, and Weitao Liu1、2、*
Author Affiliations
  • 1Department of Physics, College of Liberal Arts and Science, National University of Defense Technology, Changsha 410073, China
  • 2Interdisciplinary Center of Quantum Information, National University of Defense Technology, Changsha 410073, China
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    Applications of ghost imaging are limited by the requirement on a large number of samplings. Based on the observation that the edge area contains more information thus requiring a larger number of samplings, we propose a feedback ghost imaging strategy to reduce the number of required samplings. The field of view is gradually concentrated onto the edge area, with the size of illumination speckles getting smaller. Experimentally, images of high quality and resolution are successfully reconstructed with much fewer samplings and linear algorithm.

    1. Introduction

    Ghost imaging (GI) provides a way to obtain images with a single-pixel detector, employing second-order correlation between the illumination field and the signal from the object. Since the first realization with entangled photons[13], researchers made great developments in different aspects[414], showing its ability for lensless imaging[15] and robustness against noise[16,17], and exploring possible applications in different fields[1824]. With the illumination patterns actively controlled and computed, the detector in the reference arm can be omitted, which further simplified the system into a real single-pixel imaging system. This is called computational GI[2528]. Due to the feature of correlation, a large number of measurements are required to achieve high-quality images, which limits the performance of GI. Many methods[2935] have been proposed towards this issue. Based on the sparsity of the interested scene, compressive GI (CSGI)[30,31] has been proved to be an effective method to decrease the number of required samplings, with the cost of heavy computing consumption. Then, adaptive GI methods based on compressed sensing and wavelet trees[3133] were reported to slow down the growth of computing consumption over the size of the image. However, complicated data processing algorithms are still required, which also costs additional time after data sampling. Therefore, methods that can decrease both the number of required measurements and computation consumption are crucial for real-time imaging.

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    Junhao Gu, Shuai Sun, Yaokun Xu, Huizu Lin, Weitao Liu. Feedback ghost imaging by gradually distinguishing and concentrating onto the edge area[J]. Chinese Optics Letters, 2021, 19(4): 041102
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    Category: Imaging Systems and Image Processing
    Received: Jun. 16, 2020
    Accepted: Oct. 9, 2020
    Posted: Oct. 10, 2020
    Published Online: Jan. 11, 2021
    The Author Email: Weitao Liu (wtliu@nudt.edu.cn)