IEEE Trans Image Process. 2017 Dec;26(12):5772-5783. doi: 10.1109/TIP.2017.2745104. Epub 2017 Aug 25.
This paper presents a novel Log-Euclidean metric inspired color-to-gray conversion model for faithfully preserving the contrast details of color image, which differs from the traditional Euclidean metric approaches. In the proposed model, motivated by the fact that Log-Euclidean metric has promising invariance properties such as inversion invariant and similarity invariant, we present a Log-Euclidean metric-based maximum function to model the decolorization procedure. The Gaussian-like penalty function consisting of the Log-Euclidean metric between gradients of the input color image and transformed grayscale image is incorporated to better reflect the degree of preserving feature discriminability and color ordering in color-to-gray conversion. A discrete searching algorithm is employed to solve the proposed model with linear parametric and non-negative constraints. Extensive evaluation experiments show that the proposed method outperforms the state-of-the-art methods both quantitatively and qualitatively.
本文提出了一种新颖的基于对数欧几里得度量的彩色到灰度转换模型，用于忠实地保留彩色图像的对比度细节，与传统的欧几里得度量方法不同。在提出的模型中，受对数欧几里得度量具有反转不变性和相似不变性等有前途的不变性性质的启发，我们提出了基于对数欧几里得度量的最大函数来模拟去色过程。包含输入彩色图像和变换后的灰度图像的梯度之间的对数欧几里得度量的高斯样惩罚函数被合并，以更好地反映颜色到灰度转换中特征可辨别性和颜色顺序的保持程度。采用离散搜索算法求解具有线性参数和非负约束的所提出的模型。广泛的评估实验表明，该方法在定量和定性方面都优于最先进的方法。
