PFNEt: An unsupervised deep network for polarization image fusion

Junchao Zhang, Jianbo Shao, Jianlai Chen, Degui Yang, Buge Liang, Rongguang Liang

Research output: Contribution to journalArticle

Abstract

Image fusion is the key step to improve the performance of object detection in polarization images. We propose an unsupervised deep network to address the polarization image fusion issue. The network learns end-to-end mapping for fused images from intensity and degree of linear polarization images, without the ground truth of fused images. Customized architecture and loss function are designed to boost performance. Experimental results show that our proposed network outperforms other state-of-the-art methods in terms of visual quality and quantitative measurement.

Original languageEnglish (US)
Pages (from-to)1507-1510
Number of pages4
JournalOptics letters
Volume45
Issue number6
DOIs
StatePublished - Mar 15 2020

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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