Huang Yinuo, Krishnan Gokul, Goswami Saurabh, Javidi Bahram
Opt Express. 2024 Jan 15;32(2):1489-1500. doi: 10.1364/OE.512438.
We propose a diffuser-based lensless underwater optical signal detection system. The system consists of a lensless one-dimensional (1D) camera array equipped with random phase modulators for signal acquisition and one-dimensional integral imaging convolutional neural network (1DInImCNN) for signal classification. During the acquisition process, the encoded signal transmitted by a light-emitting diode passes through a turbid medium as well as partial occlusion. The 1D diffuser-based lensless camera array is used to capture the transmitted information. The captured pseudorandom patterns are then classified through the 1DInImCNN to output the desired signal. We compared our proposed underwater lensless optical signal detection system with an equivalent lens-based underwater optical signal detection system in terms of detection performance and computational cost. The results show that the former outperforms the latter. Moreover, we use dimensionality reduction on the lensless pattern and study their theoretical computational costs and detection performance. The results show that the detection performance of lensless systems does not suffer appreciably. This makes lensless systems a great candidate for low-cost compressive underwater optical imaging and signal detection.
我们提出了一种基于漫射器的无透镜水下光信号检测系统。该系统由一个配备随机相位调制器用于信号采集的无透镜一维（1D）相机阵列和一个用于信号分类的一维积分成像卷积神经网络（1DInImCNN）组成。在采集过程中，由发光二极管发射的编码信号穿过浑浊介质以及部分遮挡物。基于一维漫射器的无透镜相机阵列用于捕获传输的信息。然后通过1DInImCNN对捕获的伪随机图案进行分类，以输出所需信号。我们在检测性能和计算成本方面，将我们提出的水下无透镜光信号检测系统与等效的基于透镜的水下光信号检测系统进行了比较。结果表明，前者优于后者。此外，我们对无透镜图案进行降维，并研究它们的理论计算成本和检测性能。结果表明，无透镜系统的检测性能没有明显下降。这使得无透镜系统成为低成本压缩水下光学成像和信号检测的理想选择。
