Binary Ghost Imaging Based on the Fuzzy Integral Method
Source abstract
Abstract
The reconstruction quality of binary ghost imaging depends on the speckle binarization process. In order to obtain better binarization speckle and improve the reconstruction quality of binary ghost imaging, a local adaptive binarization method based on the fuzzy integral is proposed in this study. There are three steps in the proposed binarization process. The first step is to calculate the integral image of the speckle with the summed-area table algorithm. Secondly, the fuzzy integral image is calculated through the discrete Choquet integral. Finally, the binarization threshold of each pixel of the speckle is selected based on the calculated fuzzy integral result. The experiment results verify the feasibility of the proposed method. Compared with other methods qualitatively and quantitatively, the proposed method has high performance in reconstructing image of target.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Random lasers and scattering media · Advanced Optical Imaging Technologies · Image and Video Quality Assessment
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
Beijing Institute of Remote Sensing Equipment, Beijing 100005, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Zhejiang Province · Key Laboratory Foundation · Fundamental Research Funds of Zhejiang Sci-Tech University
References (49)
Show source-provided references
1. Ghost imaging: From quantum to classical to computational10.1364/AOP.2.000405 ↗
2. Ghost imaging with thermal light by third-order correlation10.1103/PhysRevA.76.043828 ↗
3. Role of Entanglement in two-photon imaging10.1103/PhysRevLett.87.123602 ↗
4. Identifying entanglement using quantum ghost interference and imaging10.1103/PhysRevLett.92.233601 ↗
5. High-resolution ghost image and ghost diffraction experiments with thermal light10.1103/PhysRevLett.94.183602 ↗
6. Experimental observation of classical subwavelength interference with a pseudothermal10.1103/PhysRevLett.94.173601 ↗
7. Computational ghost imaging10.1103/PhysRevA.78.061802 ↗
8. The physics of ghost imaging10.1007/s11128-011-0356-5 ↗
9. Multispectral computational ghost imaging with multiplexed illumination10.1088/2040-8986/aa72ff ↗
10. Multispectral imaging using a single bucket detector10.1038/srep24752 ↗
11. Hyperspectral computational ghost imaging via temporal multiplexing10.1109/LPT.2015.2494878 ↗
12. Hyperspectral terahertz microscopy via nonlinear ghost imaging10.1364/OPTICA.381035 ↗
13. Gongora, J.S.T., Olivieri, L., Peters, L., Tunesi, J., Cecconi, V., Cutrona, A., Tucker, R., Pasquazi, A., and Peccianti, M. (2020). Route to intelligent imaging reconstruction via terahertz nonlinear ghost imaging. Micromachines, 11.10.3390/mi11050521 ↗
14. A single-pixel terahertz imaging system based on compressed sensing10.1063/1.2989126 ↗
15. Computational ghost imaging for remote sensing10.1364/JOSAA.29.000782 ↗
16. Ghost imaging lidar via sparsity constraints10.1063/1.4757874 ↗
17. Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence10.1088/1612-202X/aac54a ↗
18. Heterodyne 3D ghost imaging10.1016/j.optcom.2016.01.049 ↗
19. Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method10.1088/1674-1056/24/12/124202 ↗
20. Phase-coded modulation 3D ghost imaging10.1016/j.ijleo.2020.165184 ↗
21. Performance analysis of ghost imaging lidar in background light environment10.1364/PRJ.5.000431 ↗
22. Wang, C., Mei, X., Pan, L., Wang, P., Li, W., Gao, X., Bo, Z., Chen, M., Gong, W., and Han, S. (2018). Airborne near infrared three-dimensional ghost imaging lidar via sparsity constraint. Remote Sens., 10.10.3390/rs10050732 ↗
23. Ghost imaging LiDAR via sparsity constraints using push-broom scanning10.1364/OE.27.013219 ↗
24. Li, J., Gao, W., Qian, J., Guo, Q., Xi, J., and Ritz, C.H. (2019). Robust entangled-photon ghost imaging with compressive sensing. Sensors, 19.10.3390/s19010192 ↗
25. Compressive sensing ghost imaging based on image gradient10.1016/j.ijleo.2019.01.067 ↗
26. Single-frame wide-field nanoscopy based on ghost imaging via sparsity constraints10.1364/OPTICA.6.001515 ↗
27. The Reconstruction Quality Improvement of Single-Pixel Imaging via Modified Split-Bregman Iteration10.1109/ACCESS.2020.2997678 ↗
28. Optical image transformation and encryption by phase-retrieval-based double random-phase encoding and compressive ghost imaging10.1016/j.optlaseng.2017.07.015 ↗
29. High imaging quality of Fourier single pixel imaging based on generative adversarial networks at low sampling rate10.1016/j.optlaseng.2021.106533 ↗
30. Deep-learning-based ghost imaging10.1038/s41598-017-18171-7 ↗
31. Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging10.1364/OE.27.025560 ↗
32. Ghost imaging based on deep learning
33. Deep-learning denoising computational ghost imaging10.1016/j.optlaseng.2020.106183 ↗
34. Binary detection in ghost imaging with preserved grayscale10.1140/epjd/e2019-90592-3 ↗
35. Image quality recovery in binary ghost imaging by adding random noise10.1364/OL.42.001640 ↗
36. Grayscale object authentication based on ghost imaging using binary signals10.1209/0295-5075/110/44002 ↗
37. Imaging quality enhancement in binary ghost imaging using the Otsu algorithm10.1088/2040-8986/aba22e ↗
38. Detail reconstruction in binary ghost imaging by using point-by-point method10.1016/j.optcom.2020.126611 ↗
39. Information fusion in computer vision using the fuzzy integral10.1109/21.57289 ↗
40. Choquet integral with respect to a symmetric fuzzy measure of a function on the real line10.1007/s10479-012-1166-6 ↗
41. Perez-Ornelas, F., Mendoza, O., Melin, P., Castro, J.R., Rodriguez-Diaz, A., and Castillo, O. (2015). Fuzzy index to evaluate edge detection in digital images. PLoS ONE, 10.10.1371/journal.pone.0131161 ↗
42. Sugeno integral generalization applied to improve adaptive image binarization10.1016/j.inffus.2020.10.020 ↗
43. Alignment-free method for DNA sequence clustering using Fuzzy integral similarity
44. Grabisch, M. (1994, January 30). Fuzzy integrals as a generalized class of order filters. Proceedings of the SPIE 2345, Image and Signal Processing for Remote Sensing, Rome, Italy.
45. Fuzzy measures and fuzzy integrals: An overview10.1080/03081079008935106 ↗
46. Adaptive thresholding using the integral image10.1080/2151237X.2007.10129236 ↗
47. Kasagi, A., Nakano, K., and Ito, Y. (2014, January 9–12). Parallel algorithms for the summed area table on the asynchronous hierarchical memory machine, with GPU implementations. Proceedings of the 2014 43rd International Conference on Parallel Processing, Minneapolis, MN, USA.10.1109/ICPP.2014.34 ↗
48. General adaptive neighborhood Choquet image filtering10.1007/s10851-009-0163-0 ↗
49. Image quality assessment: From error visibility to structural similarity10.1109/TIP.2003.819861 ↗