TY - GEN
T1 - D-HAN
T2 - 2023 Photonics North, PN 2023
AU - Marquez, Miguel
AU - Lai, Yingming
AU - Liu, Xianglei
AU - Jiang, Cheng
AU - Zhang, Shian
AU - Arguello, Henry
AU - Liang, Jinyang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Snapshot compressive imaging (SCI) has surged as a crucial tool for data visualization in applications limited to a single-shot. However, most existing methods shortfall in the reiterated use of random coded apertures and the idealization of the shearing function behavior. To overcome these limitations, we develop a new end-to-end convolutional neural network, termed deep high-dimensional adaptive net (D-HAN), that supplies the SCI systems with multifaceted supervision in the encoding operation, the shearing process, and the reconstruction. D-HAN is implemented in a representative SCI system for hyperspectral and ultrahigh-speed imaging.
AB - Snapshot compressive imaging (SCI) has surged as a crucial tool for data visualization in applications limited to a single-shot. However, most existing methods shortfall in the reiterated use of random coded apertures and the idealization of the shearing function behavior. To overcome these limitations, we develop a new end-to-end convolutional neural network, termed deep high-dimensional adaptive net (D-HAN), that supplies the SCI systems with multifaceted supervision in the encoding operation, the shearing process, and the reconstruction. D-HAN is implemented in a representative SCI system for hyperspectral and ultrahigh-speed imaging.
KW - Snapshot compressive imaging
KW - coded aperture design
KW - end-to-end neural networks
KW - shearing estimation
UR - https://www.scopus.com/pages/publications/85171580273
U2 - 10.1109/PN58661.2023.10222945
DO - 10.1109/PN58661.2023.10222945
M3 - 会议稿件
AN - SCOPUS:85171580273
T3 - 2023 Photonics North, PN 2023
BT - 2023 Photonics North, PN 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 June 2023 through 15 June 2023
ER -