{'fraction_non_alphanumeric': 0.026336173508907823, 'fraction_numerical': 0.0038729666924864447, 'mean_word_length': 5.65979381443299, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Depth acquisition in single-pixel imaging with multiplexed illumination.

Single-pixel imaging (SPI) has drawn wide attentions due to its high signal-to-noise ratio and wide working spectrum, providing a feasible solution when array sensors are expensive or not available. In the conventional SPI, the target's depth information is lost in the acquisition process due to the 3D-to-1D projection. In this work, we report an efficient depth acquisition method that enables the existing SPI systems to obtain reflectance and depth information without any additional hardware. The technique employs a multiplexed illumination strategy that contains both random and sinusoidal codes, which simultaneously encode the target's spatial and depth information into the single measurement sequence. In the reconstruction phase, we build a convolutional neural network to decode both spatial and depth information from the 1D measurements. Compared to the conventional scene acquisition method, the end-to-end deep-learning reconstruction reduces both sampling ratio (30%) and computational complexity (two orders of magnitude). Both simulations and experiments validate the method's effectiveness and high efficiency for additional depth acquisition in single-pixel imaging without additional hardware.