A Broad-Spectrum Diffractive Network via Ensemble Learning

Abstract

We proposed a broad-spectrum diffractive deep neural network (BS-D2NN) framework, which incorporates multi-wavelength channels of input lightfields and performs a parallel phase-only modulation utilizing a layered passive mask architecture. A complementary multi-channel base learner cluster is formed in a homogeneous ensemble framework based on the diffractive dispersion during lightwave modulation. In addition, both the optical Sum operation and the Hybrid (optical-electronic) Maxout operation are performed for motivating the BS-D2NN to learn and construct a mapping between input lightfields and truth labels under heterochromatic ambient lighting. The BS-D2NN can be trained using deep learning algorithms so as to perform a kind of wavelength-insensitive high-accuracy object classification.