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Adaptive partition based underwater polarization image restoration method for complex objects.

Shenghui Zhang1, Ronghua Li2,3, Yuanyi Fan1

  • 1School of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116028, China.

Scientific Reports
|November 18, 2025
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Summary

This study introduces an adaptive underwater polarization imaging method that segments complex targets. This approach improves image reconstruction quality by analyzing polarization characteristics in different regions.

Keywords:
Adaptive partitionComplex targetsImage restorationPolarization imagingUnderwater imaging

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Area of Science:

  • Optical Engineering
  • Image Processing
  • Remote Sensing

Background:

  • Underwater imaging relies on polarization information, but targets have complex, variable polarization characteristics.
  • Existing methods struggle with scenes containing both high- and low-polarization targets or regions within a single target.

Purpose of the Study:

  • To develop an adaptive partition-based underwater polarization imaging method for complex objects.
  • To enhance image reconstruction quality for targets with intricate polarization properties.

Main Methods:

  • An adaptive partition strategy using an image contribution operator (region size and error control operators).
  • Individual estimation of polarization characteristics for each partitioned region.
  • Application to underwater polarization imaging of complex targets.

Main Results:

  • The method adaptively partitions target images based on polarization characteristics.
  • Successfully improved image recovery for complex targets in various underwater scenarios.
  • Demonstrated robustness across different levels of water turbidity.

Conclusions:

  • The proposed adaptive partitioning method effectively handles complex polarization characteristics in underwater imaging.
  • Achieves superior image reconstruction quality compared to traditional methods for challenging targets.
  • Offers a robust solution for underwater target imaging in diverse environmental conditions.