Abstract
We propose a broad-spectrum diffractive deep neural network (BS-D2NN) framework, which incorporates multiwavelength channels of input lightfields and performs a parallel phase-only modulation using a layered passive mask architecture. A complementary multichannel 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 to perform a kind of wavelength-insensitive high-accuracy object classification.
© 2022 Optical Society of America
Full Article | PDF ArticleMore Like This
Jiashuo Shi, Liang Zhou, Taige Liu, Chai Hu, Kewei Liu, Jun Luo, Haiwei Wang, Changsheng Xie, and Xinyu Zhang
Opt. Lett. 46(14) 3388-3391 (2021)
Hongkun Dou, Yue Deng, Tao Yan, Huaqiang Wu, Xing Lin, and Qionghai Dai
Opt. Lett. 45(10) 2688-2691 (2020)
Shuiqin Zheng, Shixiang Xu, and Dianyuan Fan
Opt. Lett. 47(7) 1798-1801 (2022)