1D speckle-learned structured light recognition

Abstract

In this Letter, we introduce a novel, to the best of our knowledge, structured light recognition technique based on the 1D speckle information to reduce the computational cost. Compared to the 2D speckle-based recognition [J. Opt. Soc. Am. A 39, 759 (2022)], the proposed 1D speckle-based method utilizes only a 1D array (1×n pixels) of the structured light speckle pattern image (n×n pixels). This drastically reduces the computational cost, since the required data is reduced by a factor of 1/n. A custom-designed 1D convolutional neural network (1D-CNN) with only 2.4 k learnable parameters is trained and tested on 1D structured light speckle arrays for fast and accurate recognition. A comparative study is carried out between 2D speckle-based and 1D speckle-based array recognition techniques comparing the data size, training time, and accuracy. For a proof-of-concept for the 1D speckle-based structured light recognition, we have established a 3-bit free-space communication channel by employing structured light-shift keying. The trained 1D CNN has successfully decoded the encoded 3-bit gray image with an accuracy of 94%. Additionally, our technique demonstrates robust performance under noise variation showcasing its deployment in practical cost-effective realworld applications.