LEARNED MASKED ROBUST PRINCIPAL COMPONENT ANALYSIS MODEL FOR INFRARED SMALL TARGET DETECTION

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-l(1) - l(1) minimization. Numerical experiments show that our proposed LMRPCA can segment and locate the targets precisely.