The underwater environment is extremely complex and variable, which makes it difficult for underwater robots detecting or recognizing surroundings using images acquired with cameras. Ghost imaging as a new imaging technique has attracted much attention due to its special physical properties and potential for imaging of objects in optically harsh or noisy environments. In this work, we experimentally study three categories of image reconstruction methods of ghost imaging for objects of different transmittance. For high-transmittance objects, the differential ghost imaging is more efficient than traditional ghost imaging. However, for low-transmittance objects, the reconstructed images using traditional ghost imaging and differential ghost imaging algorithms are both exceedingly blurred and cannot be improved by increasing the number of measurements. A compressive sensing method named augmented Lagrangian and alternating direction algorithm (TVAL3) is proposed to reduce the background noise imposed by the low-transmittance. Experimental results show that compressive ghost imaging can dramatically subtract the background noise and enhance the contrast of the image. The relationship between the quality of the reconstructed image and the complexity of object itself is also discussed.

中文翻译：

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用于检测低透光率物体的鬼成像增强

水下环境极其复杂多变，这使得水下机器人难以利用摄像头获取的图像来检测或识别周围环境。鬼成像作为一种新的成像技术，由于其特殊的物理特性和在光学恶劣或嘈杂环境中对物体成像的潜力而备受关注。在这项工作中，我们实验研究了三种不同透光率物体的鬼成像图像重建方法。对于高透光率物体，差分重影成像比传统重影成像更有效。然而，对于低透光率物体，使用传统重影成像和差分重影成像算法重建的图像都非常模糊，无法通过增加测量次数来改善。提出了一种称为增强拉格朗日和交替方向算法（TVAL3）的压缩感知方法来减少低透射率带来的背景噪声。实验结果表明，压缩重影成像可以显着减少背景噪声，增强图像的对比度。还讨论了重建图像的质量与对象本身的复杂性之间的关系。