Wang Wei, Li Zhengguo, Wu Shiqian, Zeng Liangcai
IEEE Trans Image Process. 2019 Sep 12. doi: 10.1109/TIP.2019.2939946.
It is challenging to convert a hazy color image into a gray-scale image because the color contrast field of a hazy image is distorted. In this paper, a novel decolorization algorithm is proposed to transfer a hazy image into a distortionrecovered gray-scale image. To recover the color contrast field, the relationship between the restored color contrast and its distorted input is presented in CIELab color space. Based on this restoration, a nonlinear optimization problem is formulated to construct the resultant gray-scale image. A new differentiable approximation solution is introduced to solve this problem with an extension of the Huber loss function. Experimental results show that the proposed algorithm effectively preserves the global luminance consistency while represents the original color contrast in gray-scales, which is very close to the corresponding ground truth gray-scale one.
将模糊的彩色图像转换为灰度图像具有挑战性，因为模糊图像的颜色对比度场会失真。本文提出了一种新颖的去色算法，将模糊图像转换为失真恢复的灰度图像。为了恢复颜色对比度场，在CIELab颜色空间中给出了恢复后的颜色对比度与其失真输入之间的关系。基于这种恢复，构建了一个非线性优化问题来构造最终的灰度图像。引入了一种新的可微近似解，通过扩展Huber损失函数来解决这个问题。实验结果表明，该算法有效地保持了全局亮度一致性，同时在灰度中呈现了原始的颜色对比度，与相应的真实灰度图像非常接近。
