Using single-pixel detection, the end-to-end neural network that jointly optimizes both encoding and decoding enables high-precision imaging and high-level semantic sensing. However, for varied sampling rates, the large-scale network requires retraining that is laboursome and computation-consuming. In this letter, we report a weighted optimization technique for dynamic rate-adaptive single-pixel imaging and sensing, which only needs to train the network for one time that is available for any sampling rates. Specifically, we introduce a novel weighting scheme in the encoding process to characterize different patterns' modulation efficiency. While the network is training at a high sampling rate, the modulation patterns and corresponding weights are updated iteratively, which produces optimal ranked encoding series when converged. In the experimental implementation, the optimal pattern series with the highest weights are employed for light modulation, thus achieving highly-efficient imaging and sensing. The reported strategy saves the additional training of another low-rate network required by the existing dynamic single-pixel networks, which further doubles training efficiency. Experiments on the MNIST dataset validated that once the network is trained with a sampling rate of 1, the average imaging PSNR reaches 23.50 dB at 0.1 sampling rate, and the image-free classification accuracy reaches up to 95.00\% at a sampling rate of 0.03 and 97.91\% at a sampling rate of 0.1.
翻译:使用单像素检测,联合优化编码和解码的端到端神经网络,可以优化编码和解码,实现高精确度成像和高语义感测;然而,对于不同取样率,大型网络需要劳动和计算费的再培训;在本信中,我们报告一个用于动态适应速率的单像素成像和感测的加权优化技术,只需对任何取样率进行一次一次性的培训;具体地说,我们在编码过程中引入一个新的加权计划,以说明不同模式的调控效率。虽然网络正在以高采样率进行训练,但调控模式和相应的重量是迭接式更新的,在趋同时产生最佳的排序编码序列。在实验实施过程中,使用具有最高重量的最佳模式序列进行轻调,从而实现高效的成像和感测。所报告的战略节省了现有动态单像素网络所要求的另一个低率网络的额外培训,这进一步提高了培训效率。在MINIS的抽样率1、0.03和0.1的标定率上对MIS标准进行了实验,在经过培训后,将P-0.01和0.1的标定的标率率提高到了网络的平均比例。