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Multi-Scale Strengthened Directional Difference Algorithm Based on the Human Vision System.

Yuye Zhang1, Ying Zheng1, Xiuhong Li1

  • 1Information Science and Engineering Department, Xinjiang University, Urumqi 830017, China.

Sensors (Basel, Switzerland)
|December 23, 2022
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Summary
This summary is machine-generated.

This study introduces a new multi-scale strengthened directional difference (MSDD) algorithm for infrared small target detection. The MSDD algorithm effectively enhances target detection in complex backgrounds by suppressing noise and clutter.

Keywords:
human visual system (HVS)infrared imagemulti-scale strengthened directional difference (MSDD)small target detection

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Area of Science:

  • Computer Vision
  • Signal Processing
  • Infrared Imaging

Background:

  • Human visual system (HVS) mechanisms are used in infrared small target detection.
  • Existing HVS-based algorithms struggle with directional information, highlight noise, and object edges.

Purpose of the Study:

  • To propose a novel multi-scale strengthened directional difference (MSDD) algorithm.
  • To improve infrared small target detection performance in complex backgrounds.

Main Methods:

  • The MSDD algorithm comprises local directional intensity measure (LDIM) and local directional fluctuation measure (LDFM).
  • LDIM uses an improved window to suppress clutter and enhance targets.
  • LDFM considers target-background characteristics to highlight signals and suppress clutter.
  • A saliency map is generated by fusing LDIM and LDFM maps.
  • Adaptive threshold segmentation is used for final target capture.

Main Results:

  • The proposed MSDD algorithm demonstrates superior detection performance compared to classical methods.
  • The algorithm effectively suppresses edge clutter, highlight noise, and holes.
  • It enhances true target signals while mitigating background interference.

Conclusions:

  • The MSDD algorithm offers a significant advancement in infrared small target detection.
  • It provides robust performance in complex background scenarios.
  • The method effectively addresses limitations of existing HVS-based approaches.