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感知照明-结构补丁分解用于增强复杂的照明水下图像

Xiangyu Deng, Kexin Zhu, Shaowei Rong

Journal of the Optical Society of America. A, Optics, image science, and vision

|January 31, 2025

概括

这项研究引入了一个新的模型来增强水下图像,提高亮度和细节. 感知照明结构补丁分解 (PISPD) 方法有效地应对复杂照明条件下的挑战.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
水下成像技术的研究.

背景情况:

水下图像因复杂的照明而退化,导致不均的曝光,颜色投射,低对比度和模糊的细节.
现有的增光方法往往无法充分照亮黑暗区域或抑制过度曝光的区域.

研究的目的:

提出一种用于增强复杂照明水下图像的新型模型.
解决现有方法在处理不均暴露和保存细节方面的局限性.

主要方法:

提出了一个感知照明结构补丁分解 (PISPD) 模型.
PISPD方法使用互补的输入:一个对比度增强的图像和一个细节利的图像.
它将输入分解为感知照明图,对比度,结构和平均强度,并包含加权边缘保护因子.

主要成果:

PISPD模型有效地平衡了亮度,并整合了来自互补输入的特征.
引入了一个新的基准数据集,CLUID,包括990个复杂的照明水下图像.
实验结果表明,PISPD在八种最先进的方法中表现优越.

结论:

PISPD模型在增强复杂的照明水下图像方面取得了重大进展.
该方法成功地减轻了常见的水下图像退化,如不均的曝光和低对比度.
引入的CLUID数据集有助于进一步研究水下图像增强.

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