| Reconstruction method of computational ghost imaging based on non-local generalized total variation北大核心CSCD |
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| 引用本文： | 蒋宗铧,田昕,杨晋陵. Reconstruction method of computational ghost imaging based on non-local generalized total variation北大核心CSCD[J]. 应用光学, 2022, 43(1): 52-59. DOI: 10.5768/JAO202243.0102001 |
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| 作者姓名： | 蒋宗铧 田昕 杨晋陵 |
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| 作者单位： | 武汉大学 电子信息学院，湖北 武汉 430072 |
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| 基金项目： | 湖北省自然科学基金(2018CFB435)。 |
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| 摘 要： | 鬼成像是一种能够透过大雾等恶劣环境的成像技术。针对传统鬼成像重建图像存在噪声较多、图像对比度较低等问题，将非局部广义全变分方法用于鬼成像的图像重建之中，提出基于非局部广义全变分的计算鬼成像重建方法。所提方法构造了一种非局部相关性权重设计梯度算子，将其代入全变分重建算法中，使得重建的图像能有效去除噪声的同时实现细节较好的还原。首先在不同条件下进行仿真模拟，得到所提方法的峰值信噪比相对其他方法提升1 dB左右，且具有更好的主观视觉效果，进而设计并搭建实验平台对算法的有效性进行验证，实验结果证明了所提方法在去除噪声和细节重建等方面的优越性。
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| 关 键 词： | 计算鬼成像 压缩感知 非局部广义全变分 空间光调制器 |
| 收稿时间： | 2021-08-05 |
Reconstruction method of computational ghost imaging based on non-local generalized total variation |
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| JIANG Zonghua,TIAN Xin,YANG Jinling. Reconstruction method of computational ghost imaging based on non-local generalized total variation[J]. Journal of Applied Optics, 2022, 43(1): 52-59. DOI: 10.5768/JAO202243.0102001 |
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| Authors: | JIANG Zonghua TIAN Xin YANG Jinling |
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| Affiliation: | School of Electronic Information, Wuhan University, Wuhan 430072, China |
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| Abstract: | The ghost imaging is an imaging technology that can penetrate harsh environments such as the heavy fog. Aiming at the problems of more noise and lower image contrast of reconstructed images of traditional ghost imaging, the non-local generalized total variation method was applied for image reconstruction of ghost imaging, and the reconstruction method of computational ghost imaging based on non-local generalized total variation was proposed. The method constructed the non-local correlation weights to design the gradient operator, which was substituted into total variation reconstruction algorithm, so that the reconstructed images could effectively remove the noise while achieving the better detail restoration. The simulations were performed under different conditions, and the peak signal-to-noise ratio of proposed method was improved by about 1 dB compared with other methods, while it had better subjective visual effects. The experimental platform was designed and built to verify the effectiveness of the algorithm. The experimental results verify the superiority of the proposed method in terms of noise removal and detail reconstruction. |
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| Keywords: | computational ghost imaging compressive sensing non-local generalized total variation spatial light modulator |
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