Super-wide-field-of-view long-wave infrared gaze polarization imaging embedded in a multi-strategy detail feature extraction and fusion network

Recently, infrared polarization imaging technology has become a research hotspot due to its ability to better resolve the physicochemical properties of objects and significantly enhance the target characteristics. However, the traditional infrared polarization imaging is limited to similar imaging mechanism restrictions, and it is difficult to acquire the polarization information of a wide-area posture in real time. Therefore, we report a combination of hardware and software for super-wide-field-of-view long-wave infrared gaze polarization imaging technology. Utilizing the non-similar imaging theory and adopting the inter-lens coupling holographic line-grid infrared polarization device scheme, we designed the infrared gazing polarized lens with a field-of-view of over 160°. Based on the fusion of infrared intensity images and infrared polarization images, a multi-strategy detail feature extraction and fusion network is constructed. Super-wide-field-of-view (150°×120°), large face array (1040×830), detail-rich infrared fusion images are acquired during the test. We have accomplished the tasks of vehicle target detection and infrared camouflage target recognition efficiently using the fusion images, and verified the superiority of recognizing far-field targets. Our implementation should enable and empower applications in machine vision, intelligent driving, and target detection under complex environments.

Authors

Institutions

Publication Details

Journal
Photonics Research
Published
2025-04-24
DOI
https://doi.org/10.1364/prj.559833
Citations
4
Primary Topic
Optical Systems and Laser Technology
Type
article
Field-Weighted Citation Impact
2.92
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Super-wide-field-of-view long-wave infrared gaze polarization imaging embedded in a multi-strategy detail feature extraction and fusion network

Bing Bing Zhou, Jun Zou, Jinhang Zhang, Li Li et al.
4 citations
Photonics Research
Optical Systems and Laser Technology
2.92
article

Super-wide-field-of-view long-wave infrared gaze polarization imaging embedded in a multi-strategy detail feature extraction and fusion network

Bing Bing Zhou, Jun Zou, Jinhang Zhang, Li Li, Limin Liu, Gang Li, Dongdong Shi, Yudan Chen
article en
4 citations

Abstract

Recently, infrared polarization imaging technology has become a research hotspot due to its ability to better resolve the physicochemical properties of objects and significantly enhance the target characteristics. However, the traditional infrared polarization imaging is limited to similar imaging mechanism restrictions, and it is difficult to acquire the polarization information of a wide-area posture in real time. Therefore, we report a combination of hardware and software for super-wide-field-of-view long-wave infrared gaze polarization imaging technology. Utilizing the non-similar imaging theory and adopting the inter-lens coupling holographic line-grid infrared polarization device scheme, we designed the infrared gazing polarized lens with a field-of-view of over 160°. Based on the fusion of infrared intensity images and infrared polarization images, a multi-strategy detail feature extraction and fusion network is constructed. Super-wide-field-of-view (150°×120°), large face array (1040×830), detail-rich infrared fusion images are acquired during the test. We have accomplished the tasks of vehicle target detection and infrared camouflage target recognition efficiently using the fusion images, and verified the superiority of recognizing far-field targets. Our implementation should enable and empower applications in machine vision, intelligent driving, and target detection under complex environments.

Photonics ResearchVol. 13(7)
PLA Army Engineering University (CN)
Openalex Percentile: Top 9%
Optical Systems and Laser Technology
2.92
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.