Article
Optics
Teng Jiang, Yanfeng Bai, Wei Tan, Xiaohui Zhu, Xianwei Huang, Suqin Nan, Xiquan Fu
Summary: Research on practical applications of ghost imaging lidar system, especially in longer sensing distance, has become urgent in recent years. In this paper, we have developed a ghost imaging lidar system that significantly improves the transmission distance of the collimated pseudo-thermal beam over long range. By adjusting the lens assembly, the system can also generate a wide field of view suitable for short-range imaging. Experimental analysis and verification of the changing tendency of illuminating field of view, energy density, and reconstructed images are presented. Some considerations for improving this lidar system are also discussed.
Article
Physics, Multidisciplinary
Hao Zhang, Yunjie Xia, Deyang Duan
Summary: In this study, a novel approach using a deep convolutional generative adversarial network for compressed sensing algorithm is proposed to enhance the imaging performance of computational ghost imaging. The results show significant improvement in image quality and effective noise elimination.
Article
Optics
Peixia Zheng, Qilong Tan, Hong-chao Liu
Summary: An inverse computational ghost imaging (GI) scheme is proposed in this study, where bucket signals are selected first and random patterns are calculated correspondingly. Compared with traditional computational GI, the inverse GI not only disguises bucket signals but also provides an opportunity to combine with other cryptographies, enriching the encryption process and enhancing security.
Article
Optics
Hao Zhang, Deyang Duan
Summary: A novel computational ghost imaging scheme utilizing a convolutional neural network-based compressed sensing algorithm was proposed to significantly improve imaging quality. Experimental results demonstrated that the scheme could produce higher quality images with the same sampling, outperforming conventional CGI.
CHINESE OPTICS LETTERS
(2021)
Article
Optics
Zhiyuan Ye, Peixia Zheng, Wanting Hou, Dian Sheng, Weiqi Jin, Hong-Chao Liu, Jun Xiong
Summary: This paper introduces a new technology called computational convolutional ghost imaging (CCGI), which uses a single-pixel photodetector and structured illumination to perform convolution operations, enabling direct observation of the features of interest in a target without imaging. The CCGI scheme can adaptively work under sub-Nyquist sampling conditions and is suitable for real-time non-imaging edge detection.
OPTICS AND LASERS IN ENGINEERING
(2022)
Article
Optics
Zhuo Yu, Chao Gao, Xiao-Qian Wang, Huan Zhao, Zhi-Hai Yao
Summary: We propose a Hadamard-2D Haar transform computational ghost imaging scheme that uses rearranged Hadamard illumination pattern for sampling. This scheme directly obtains the 2D-Haar wavelet coefficients by performing a low-order Hadamard transform on the acquired signal and retrieves the information of the target object through the inverse transform. Theoretical and experimental proof shows that this method is more robust to external noise and has advantages in reconstructing images at low sampling rates due to the extraction property of edge information by 2D-Haar wavelet.
OPTICS AND LASER TECHNOLOGY
(2022)
Article
Environmental Sciences
Wanli Xue, Zhe Zhang, Shengyong Chen
Summary: This study proposes a novel image stitching method that can identify and eliminate ghosts through multi-component collaboration without object distortion, segmentation, or repetition, and validates the effectiveness of the proposed method through experiments.
Article
Optics
Song-Ming Yan, Ming-Jie Sun, Wen Chen, Li-Jing Li
Summary: The proposed method calibrates non-uniform illumination in computational ghost imaging by capturing an image of an all-white paper and using that information to calibrate further reconstructed images under the same illumination. Experimental results showed a 79.94% reduction in root mean square error of the reconstructed image after calibration.
Article
Optics
Xuan Liu, Tailin Han, Cheng Zhou, Jun Hu, Mingchi Ju, Bo Xu, Lijun Song
Summary: This study introduces a novel computational ghost imaging method based on deep learning technology and array detector measurement, which can achieve high-quality imaging even at low sampling rates. The method solves the issues of pixel misalignment and information loss in reconstructed images, and is applicable in real-time detection and biomedical imaging.
Article
Optics
Anrun Yang, Yuan Zhang, Lirong Ren, Fangqiong Li, Yuanyuan Wu, Lei Wu, Dejian Zhang, Jiangtao Liu
Summary: This study examines the influence of negative and positive film imaging on ghost imaging quality. The results show that when the object to be imaged is black and white, negative film provides significantly better imaging effect than positive film, increasing the contrast to noise ratio by about 241% or reducing the sampling times by 5 under the same image quality. The main reason is the greater absolute and relative intensity fluctuation in negative film imaging. When the object to be imaged is in color, using a reverse color light source with the background color of the object can achieve a similar negative imaging effect, improving the imaging quality and increasing the contrast to noise ratio by 52%-69%.
Article
Optics
Heng Wu, Genping Zhao, Meiyun Chen, Lianglun Cheng, Huapan Xiao, Limin Xu, Daodang Wang, Jian Liang, Yiping Xu
Summary: The study introduces a hybrid neural network-based adaptive computational ghost imaging (CGI) method, which can restore clear images of objects with different sub-Nyquist sampling ratios. By utilizing an interference-adding layer in the network, the method effectively removes multiple degradations and noise during training, improving the efficiency and quality of image reconstruction.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Optics
Shoupei Liu, Xiangfeng Meng, Yongkai Yin, Huazheng Wu, Wenjie Jiang
Summary: UNNCGI is a computational ghost imaging method that employs an untrained neural network to generate high-quality images even at low sampling ratios, improving imaging efficiency.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Optics
Wenxiu Wan, Chunling Luo, Fumin Guo, Jian Zhou, Peilin Wang, Xiaoyan Huang
Summary: Imaging through strong scattering media is a difficult challenge in optical imaging, but asynchronous computational ghost imaging (ACGI) offers a novel solution by capturing the ghost image of an unknown object using asynchronous detection. To improve image quality, the ACGI system uses a compressed sensing algorithm to reconstruct sharp ghost images with fewer measurements.
OPTICS AND LASER TECHNOLOGY
(2022)
Article
Optics
Wangtao Yu, Dekui LI, Kai Guo, Zhipeng Yin, Zhongyi Guo
Summary: Computational ghost imaging (CGI) can reconstruct scene images by correlating sampling patterns and detected intensities. Two new sampling methods, CSP-CGI and HCSP-CGI, are proposed to achieve high-quality CGI under low sampling rates. The methods significantly reduce the sampling number and enable real-time ghost imaging. Experimental results demonstrate their superiority over existing methods in both qualitative and quantitative aspects.
Article
Chemistry, Analytical
Yingqiang Zhang, Jie Cao, Huan Cui, Dong Zhou, Bin Han, Qun Hao
Summary: Computational ghost imaging provides a way to reconstruct images with the spatial distribution information of illumination patterns, and a proposed method using time-variant retina-like patterns improves the reconstruction quality for axially moving targets.
