论文标题
最大化差分熵的盲逆伽马校正
Blind Inverse Gamma Correction with Maximized Differential Entropy
论文作者
论文摘要
在图像获取，处理和/或显示过程中，经常出现多种图像的非线性伽玛失真。伽马失真通常随捕获设置的变化和亮度变化而变化。从给定图像中自动确定适当的恢复伽马值的盲人逆γ校正对于减轻失真至关重要。对于盲骨校正，直接从最大化的差分熵模型中提出了一种自适应伽马转化方法（AGT-ME）。并且相应的优化具有数学简洁的封闭形式解决方案，从而有效实现和准确的AGT-ME恢复了伽马。考虑到人眼具有非线性感知的灵敏度，还提出了修改的版本Agt-Me-Visual，以实现更好的视觉性能。 AGT-ME在可变数据集上进行了测试，可以准确估计大量伽马失真（0.1至3.0），表现优于最新方法。此外，提出的AGT-ME和AGT-ME-Visual被应用于三个典型应用，包括自动伽马调节，自然/医疗图像对比度增强以及边缘投影介绍图像恢复。此外，AGT-ME/ AGT-ME-Visual是一般的，可以无缝扩展到蒙版的图像，多频道（颜色或频谱）图像或多帧视频，并且没有任意调谐参数。此外，还为感兴趣的用户提供了相应的Python代码（https://github.com/yongleex/agt-me）。
Unwanted nonlinear gamma distortion frequently occurs in a great diversity of images during the procedures of image acquisition, processing, and/or display. And the gamma distortion often varies with capture setup change and luminance variation. Blind inverse gamma correction, which automatically determines a proper restoration gamma value from a given image, is of paramount importance to attenuate the distortion. For blind inverse gamma correction, an adaptive gamma transformation method (AGT-ME) is proposed directly from a maximized differential entropy model. And the corresponding optimization has a mathematical concise closed-form solution, resulting in efficient implementation and accurate gamma restoration of AGT-ME. Considering the human eye has a non-linear perception sensitivity, a modified version AGT-ME-VISUAL is also proposed to achieve better visual performance. Tested on variable datasets, AGT-ME could obtain an accurate estimation of a large range of gamma distortion (0.1 to 3.0), outperforming the state-of-the-art methods. Besides, the proposed AGT-ME and AGT-ME-VISUAL were applied to three typical applications, including automatic gamma adjustment, natural/medical image contrast enhancement, and fringe projection profilometry image restoration. Furthermore, the AGT-ME/ AGT-ME-VISUAL is general and can be seamlessly extended to the masked image, multi-channel (color or spectrum) image, or multi-frame video, and free of the arbitrary tuning parameter. Besides, the corresponding Python code (https://github.com/yongleex/AGT-ME) is also provided for interested users.
