TY - GEN
T1 - Learned Masked Robust Principal Component Analysis Model for Infrared Small Target Detection
AU - Zhou, Xinyu
AU - Zhang, Ye
AU - Hu, Yue
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We proposed a learned masked robust principal component analysis (LMRPCA) algorithm for single-frame infrared small target detection. Firstly, the original images are constructed into patch images, which are separated into low-rank and sparse components corresponding to the backgrounds and foreground masks. The optimization function is solved by alternating directions of multipliers method (ADMM), which is mapped to trainable convolutional layers. We use elements of convolutional sparse coding to improve representation learning for foreground masks and side information in the auxiliary transform domain. By doing so, we assign learnable weights to different feature maps by using a reweighted-l1 - l1 minimization. Numerical experiments show that our proposed LMRPCA can segment and locate the targets precisely.
AB - We proposed a learned masked robust principal component analysis (LMRPCA) algorithm for single-frame infrared small target detection. Firstly, the original images are constructed into patch images, which are separated into low-rank and sparse components corresponding to the backgrounds and foreground masks. The optimization function is solved by alternating directions of multipliers method (ADMM), which is mapped to trainable convolutional layers. We use elements of convolutional sparse coding to improve representation learning for foreground masks and side information in the auxiliary transform domain. By doing so, we assign learnable weights to different feature maps by using a reweighted-l1 - l1 minimization. Numerical experiments show that our proposed LMRPCA can segment and locate the targets precisely.
KW - Small infrared target
KW - deep network
KW - learned infrared patch-image model
UR - https://www.scopus.com/pages/publications/85181568238
U2 - 10.1109/IGARSS52108.2023.10282097
DO - 10.1109/IGARSS52108.2023.10282097
M3 - 会议稿件
AN - SCOPUS:85181568238
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6636
EP - 6639
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -