Gradient preserving RGB-to-gray conversion using random forest

This paper proposes a new algorithm for color-to-gray conversion preserving the gradient information in input color image. To preserve the gradient in a color image, we construct a random forest representing the relation between color intensity and gradient in an input image. The leaf nodes of random trees indicate the gray colors (single channel colors) corresponding to the input RGB colored pixels. From these initial gray colors obtained by the random forest, we determine the final gray scale by keeping the balance between intensity and luminance channels. In our experiments, we show that the proposed method outperforms the state-of-the-arts in view of color constrast preserving ratio and mean squared error versus luminance.