Incoherent Optical Neural Networks for Passive and Delay-Free Inference in Natural Light

Optical neural networks are hardware neural networks implemented based on physical optics, and they have demonstrated advantages of high speed, low energy consumption, and resistance to electromagnetic interference in the field of image processing. However, most previous optical neural networks were designed for coherent light inputs, which required the introduction of an electro-optical conversion module before the optical computing device. This significantly hindered the inherent speed and energy efficiency advantages of optical computing. In this paper, we propose a diffraction algorithm for incoherent light based on mutual intensity propagation, and on this basis, we established a model of an incoherent optical neural network. This model is completely passive and directly performs inference calculations on natural light, with the detector directly outputting the results, achieving target classification in an all-optical environment. The proposed model was tested on the MNIST, Fashion-MNIST, and ISDD datasets, achieving classification accuracies of 82.32%, 72.48%, and 93.05%, respectively, with experimental verification showing an accuracy error of less than 5%. This neural network can achieve passive and delay-free inference in a natural light environment, completing target classification and showing good application prospects in the field of remote sensing.