By combining spatial dimensions and multiple degrees of freedom, structured light can greatly improve information capacity. In recent years, the combination of structured light patterns with image processing and machine intelligence has shown great potential for development in areas such as communications and sensing.
One of the most remarkable features of structured light fields is the two-dimensional and three-dimensional distribution of their amplitude information. This feature can be effectively integrated with mature image processing technology, and thanks to the currently transformative machine learning technology, information transmission between media can also be realized. Complex structured light fields based on coherent superposition states can carry rich spatial amplitude information. By further combining spatial nonlinear transformation, a significant increase in information capacity can be achieved.
Zhang Zilong of Beijing Institute of Technology and Shen Yijie of Nanyang Technological University, together with their team members, proposed a new method to enhance information capacity based on complex-mode coherent superposition states and their spatial nonlinear transformation. By integrating machine vision and deep learning techniques, they realized large-angle point-to-multipoint information transmission with low bit error rate.
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In this model, a Gaussian beam is used to obtain spatially nonlinear transformation (SNC) of structured light through a spatial light modulator. A convolutional neural network (CNN) is used to identify the intensity distribution of the beam. Comparing the fundamental superposition mode and the SNC mode, it is observed that as the order of the constituent eigenmodes of the fundamental mode increases, the encoding ability of the HG superposition mode is significantly improved over that of the LG mode, and the mode encoding ability after spatially structured nonlinear transformation is significantly improved.
To verify the encoding and decoding performance based on the above model, a 50×50 pixel color image shown in Figure 1 was transmitted. The RGB dimension of the image was divided into five chromaticity levels, consisting of a total of 125 kinds of chromaticity information, each encoded with 125 HG coherent superposition states. Furthermore, different degrees of phase jitter induced by atmospheric turbulence were loaded into these 125 modes via the DMD spatial light modulator and trained with deep learning techniques to form a dataset.
In addition, an analysis of the effect of decoding with larger capacity was carried out using nonlinear transformation. In this analysis, 530 SNC modes were selected to experimentally measure the confusion matrix to these modes by convolutional neural networks, as shown in Figure 2. Experimental results show that with clearer structural features, the SNC mode significantly increases data capacity while ensuring a similarly low bit error rate, and the data recognition accuracy is up to 99.5%. In addition, the experiment also verified the machine vision pattern recognition ability under diffuse reflection conditions, achieving simultaneous high-precision decoding by multiple receiving cameras with observation angles up to 70°.
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