一个感知优化和自我校准的音调映射操作员
概括
这项研究引入了一种新的两级神经网络用于音调映射,增强高动态范围 (HDR) 图像以获得更好的视觉质量. 该方法优化音调映射操作员 (TMO) 以获得感知上优异的结果和高效的处理.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 计算机摄影摄影的使用
背景情况:
- 高动态范围 (HDR) 摄影越来越受欢迎,需要高效的音色映射操作员 (TMO) 来进行动态范围压缩.
- 现有的TMO在平衡视觉质量和计算速度方面面临挑战.
- 人类视觉系统 (HVS) 原则为开发知觉优化的图像处理技术提供了洞察力.
研究的目的:
- 开发一种新的,自我校准和感知优化的基于两个阶段神经网络的TMO,用于HDR图像色调映射.
- 通过与人类感知保持一致来提高色调映射图像的视觉质量.
- 为了实现与快速本地TMO可比的高性能.
主要方法:
- 采用了两阶段的神经网络方法,灵感来自人类视觉系统生理学.
- 第一阶段将HDR图像分解为正常化的拉普拉斯金字塔,并使用轻量级神经网络来估计LDR图像的拉普拉斯金字塔,通过感知度量优化.
- 第二阶段生成一个伪多曝光图像堆,并使用融合网络将它们合并,最大限度地提高结构相似度指数的变量.
主要成果:
- 与现有的TMO相比,拟议的方法始终产生具有优越视觉质量的图像.
- 在计算速度方面,开发的TMO是最快的本地TMO之一.
- 实验结果验证了所使用的优化指标的感知相关性和有效性.
结论:
- 两个阶段的神经网络TMO在HDR图像色调映射方面取得了重大进展.
- 自校准和感知优化的方法有效地提高了图像细节和颜色和度.
- 这种方法为实时HDR图像处理应用提供了有前途的解决方案.
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