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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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水下图像增强基于最佳加权的直方图框架和改进的菲克定律算法.

Yawen Liu1,2, Ziteng Qiao1, Zhiwei Ye1

  • 1School of Computer Science, Hubei University of Technology, Wuhan, 430068, China.

Scientific reports
|December 5, 2024
PubMed
概括

这项研究介绍了一种改进的基于Fick定律算法的最佳加权直方图框架 (IFLAHF),用于增强水下图像. 这种新的方法优化了参数,纠正了颜色偏差,与现有的算法相比,显著提高了图像质量.

关键词:
菲克的定律算法法基因组图均等化 基因组图均等化图像增强 图像增强 图像增强平原的边界是高原的边界.水下图像 水下图像 水下图像

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科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 海洋学 海洋学 海洋学

背景情况:

  • 水下图像质量因光散射和衰减而降低,导致对比度和亮度的损失.
  • 现有的方法,如基于双组图平衡的三平原极限 (BHE3PL),由于固定的参数而存在局限性,阻碍了适应性.

研究的目的:

  • 提出一种改进的水下图像增强方法,解决现有技术的局限性.
  • 为了增强对比度,细节和亮度,同时纠正水下图像中的色彩偏差.

主要方法:

  • 开发了一种改进的基于Fick定律算法的最佳加权直方体框架 (IFLAHF).
  • 在Fick的法则算法 (FLA) 中使用Tent混乱映射,反向学习和Levy飞行优化固定参数.
  • 集成了一种颜色校正技术,以获得自然的图像外观.

主要成果:

  • 拟议的IFLAHF方法在增强水下图像方面表现出卓越的性能.
  • 模拟证实,该方法在多个水下图像增强指标上优于现有的算法.
  • 增强的图像表现出更好的对比度,细节和颜色保真度.

结论:

  • 改进的基于菲克定律算法的最佳加权直方体框架 (IFLAHF) 为水下图像增强提供了强大的解决方案.
  • 该方法的参数优化和色彩校正功能导致图像质量的显著改善.
  • 这项工作为水下图像处理领域做出了宝贵的贡献.