Computational ghost imaging study based on incoherent light from blackbody radiation

Guang Yang, Chunyu Sui, Tingting Jia, Zhandong Liu, Zongguo Li, Hongguo Li

Article ID: 1741
Vol 5, Issue 1, 2022, Article identifier:1-7

VIEWS - 341 (Abstract) 201 (PDF)

Abstract


In recent years, ghost imaging has made important progress in the field of remote sensing imaging. In order to promote the application of solar ghost imaging in this field, this paper studies the computational ghost imaging based on the incoherent light of blackbody radiation. Firstly, according to the intensity probability density function of blackbody radiation, the expression of contrast-to-noise ratio (RCN) describing the quality of computational ghost imaging is obtained, and then the random speckle pattern simulating blackbody radiation is generated by computer with the idea of slice sampling, finally, a digital light projector is used to modulate and generate the random modulated light that simulates the blackbody radiation light source, and this light source is used to realize the computational ghost image of the reflective object in the experiment. The “ghost image” of the object under different measurement frame numbers is reconstructed, and the contrast-to-noise ratio describing the imaging quality is measured. The results show that the image quality is relatively good when the average intensity (gray) of the randomly modulated speckle is about 160. On the other hand, the contrast-to-noise ratio of the image gradually increases from 0.8795 to 1.241, 1.516, 1.755, 2.100 and 2.371 as the number of measurement frames increases from 2,000 to 4,000, 6,000, 8,000, 12,000 and 20,000, respectively. The experimental results are basically consistent with the theoretical analysis. The results are of great significance for the application of ghost imaging with incoherent light, such as sunlight, which is approximately regarded as blackbody radiation, in the field of remote imaging.


Keywords


Speckle Imaging; Blackbody Radiation; Computational Ghost Imaging; Contrast-to-noise Ratio

Full Text:

PDF


References


Pittman TB, Shih YH, Strekalov DV, et al. Optical imaging by means of two-photon quantum entan-glement. Physical Review A 1995; 52(5): R3429.

Valencia A, Scarcelli G, D’Angelo M, et al. Two-photon imaging with thermal light. Physical Review Letters 2005; 94(6): 063601.

Ferri F, Magatti D, Gatti A, et al. High-resolution ghost image and ghost diffraction experiments with thermal light. Physical Review Letters 2005; 94(18): 183602.

Zhang D, Zhai Y, Wu L, et al. Correlated two-photon imaging with true thermal light. Optics Letters 2005; 30(18): 2354–2356.

Cao D, Xiong J, Zhang S, et al. Enhancing visibility and resolution in Nth-order intensity correlation of thermal light. Applied Physics Letters 2008; 92(20): 201102.

Gao C, Wang X, Cai H, et al. Influence of random phase modulation on the imaging quality of com-putational ghost imaging. Chinese Physics B 2019; 28(2): 020201.

Wu H, Wu W, Chen M, et al. Computational ghost imaging with 4-step iterative rank minimization. Physics Letters A 2021; 394: 127199.

Zhang D, Yin R, Wang T, et al. Ghost imaging with bucket detection and point detection. Optics Communications 2018; 412: 146–149.

Huang J, Shi D, Meng W, et al. Spectral encoded computational ghost imaging. Optics Communications 2020; 474: 126105.

Shapiro JH. Computational ghost imaging. Physical Review A 2008; 78(6): 061802.

Bromberg Y, Katz O, Silberberg Y. Ghost imaging with a single detector. Physical Review A 2009; 79(5): 053840.

Sun B, Edgar M P, Bowman R, et al. 3D computational imaging with single-pixel detectors. Science 2013; 340(6134): 844–847.

Zerom P, Shi Z, O’Sullivan MN, et al. Thermal ghost imaging with averaged speckle patterns. Physical Review A 2012; 86(6): 063817.

Chan KWC, O’Sullivan MN, Boyd RW. High-order thermal ghost imaging. Optics Letters 2009; 34(21): 3343–3345.

Chan KWC, O’Sullivan MN, Boyd RW. Optimiza-tion of thermal ghost imaging: High-order correlations vs. background subtraction. Optics Express 2010; 18(6): 5562–5573.

Neal RM. Slice sampling. The Annals of Statistics 2003; 31(3): 705–767.




DOI: http://dx.doi.org/10.24294/irr.v5i1.1741

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License

This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.