Metal target detection method using passive millimeter-wave polarimetric imagery

Tang, Fangzhou; Gui, Liangqi; Liu, Jinbang; Chen, Ke; Lang, Liang; Cheng, Yayun

doi:10.1364/oe.390385

Cited by 19 publications

(5 citation statements)

References 25 publications

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“…However, optical information is not only about intensity; it also concerns polarization. Traditionally, imaging polarimeters are used for such purposes as target detection [15][16][17], 3D reconstruction [18][19][20], underwater image recovery [21,22], and remote sensing [23][24][25][26]. However, traditional frame-based imaging polarimeters have limited frame rates and dynamic ranges [27].…”

Section: Introductionmentioning

confidence: 99%

Training a Dataset Simulated Using RGB Images for an End-to-End Event-Based DoLP Recovery Network

Yan,

Wang,

Zhang

et al. 2024

Photonics

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Event cameras are bio-inspired neuromorphic sensors that have emerged in recent years, with advantages such as high temporal resolutions, high dynamic ranges, low latency, and low power consumption. Event cameras can be used to build event-based imaging polarimeters, overcoming the limited frame rates and low dynamic ranges of existing systems. Since events cannot provide absolute brightness intensity in different angles of polarization (AoPs), degree of linear polarization (DoLP) recovery in non-division-of-time (non-DoT) event-based imaging polarimeters is an ill-posed problem. Thus, we need a data-driven deep learning approach. Deep learning requires large amounts of data for training, and constructing a dataset for event-based non-DoT imaging polarimeters requires significant resources, scenarios, and time. We propose a method for generating datasets using simulated polarization distributions from existing red–green–blue images. Combined with event simulator V2E, the proposed method can easily construct large datasets for network training. We also propose an end-to-end event-based DoLP recovery network to solve the problem of DoLP recovery using event-based non-DoT imaging polarimeters. Finally, we construct a division-of-time event-based imaging polarimeter simulating an event-based four-channel non-DoT imaging polarimeter. Using real-world polarization events and DoLP ground truths, we demonstrate the effectiveness of the proposed simulation method and network.

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Section: Introductionmentioning

confidence: 99%

Training a Dataset Simulated Using RGB Images for an End-to-End Event-Based DoLP Recovery Network

Yan,

Wang,

Zhang

et al. 2024

Photonics

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“…Therefore, polarization imaging technology has a strong ability to detect targets in complex backgrounds and low visibility conditions [1][2][3]. Owing to these benefits, polarization imaging has been widely used in target detection [4,5], turbid media imaging [6,7], 3D reconstruction [8], biomedical image analysis [9][10][11] and material classification [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A new adaptive window‐based guided filtering and interpolation for polarization image demosaicing

Xie

Liu

Chen

2023

IET Image Processing

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This paper presents a new adaptive window‐based guided filtering and interpolation for polarization image demosaicing. Considering the correlation between the polarization channels, the authors use the total intensity S0 as a guide image to guide the interpolation of all channels. The guided filtering is performed with a new adaptive window size selection technique. A novel iterative residual interpolation is proposed to construct accurate full resolution polarization. Experiments are conducted with the sample images from the demosaicking algorithms dataset. Objective evaluations demonstrate that the proposed algorithm can reduce root mean squared error by at least 27% over other methods. Subjective visual inspection can also verify the improvements in the clarity of objects in angle and degree of the linear polarization images.

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“…M. Dubreuil et al improved detection performance by estimating targets using two orthogonal polarization images [8]. Tang et al proposed a bilinear polarization discriminator for detecting metal targets [9]. Sun et al introduced the distribution of polarization information as a feature factor to distinguish metal targets from natural backgrounds [10].…”

Section: Introductionmentioning

confidence: 99%

Polarization-Enhanced Underwater Detection Method for Multiple Material Targets Based on Deep-Learning

Wang,

Gao,

Chen

et al. 2023

IEEE Photonics J.

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Underwater target detection is an essential topic in the applications of underwater exploration. However, underwater target detection faces serious challenges, such as complex scattering, low visibility, and target clutter. Here a polarization-enhanced underwater multiple material target detection method is proposed to address these challenges. The similarity principle of locally backscattered polarization features is utilized to suppress the influence of backscattered light. Our target detection model combines polarization gradient and edge detection techniques to optimize the detection process, enabling superior target detection and feature extraction. Experimental results indicate that our method has significantly enhanced the detection performance in multiple (overlapping or nonoverlapping) material targets, especially in high turbid underwater scattering environments. This research provides a promising new approach for polarized target detection in underwater environments and opens up new possibilities for underwater multiple-material target detection.

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Metal target detection method using passive millimeter-wave polarimetric imagery

Cited by 19 publications

References 25 publications

Training a Dataset Simulated Using RGB Images for an End-to-End Event-Based DoLP Recovery Network

Training a Dataset Simulated Using RGB Images for an End-to-End Event-Based DoLP Recovery Network

A new adaptive window‐based guided filtering and interpolation for polarization image demosaicing

Polarization-Enhanced Underwater Detection Method for Multiple Material Targets Based on Deep-Learning

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