Adaptive grayscale method based on weighted guided filtering

The essence of grayscale is to convert a three-dimensional color image into a one-dimensional grayscale image, and information loss is inevitable, so it is necessary to preserve the contrast, detail and structure of the original color image as much as possible. In this paper, we propose a new contrast retention grayscale method based on weighted guided filtering. Firstly, considering the different sensitivity of the human visual system to the three color channels of R, G, and B and the different information contained in the images of each channel, a new contrast feature coefficient is defined to better retain the contrast characteristics of the original color image. Secondly, in order to make the grayscale image have a better sense of hierarchy, the saturation feature holding term is designed. Finally, the discrete search strategy is used to optimize the proposed new model, and on this basis, weighted guided filtering is used to enhance the edge details of grayscale images. A large number of experimental results on the three datasets show that the grayscale effect of the proposed method is better than that of the traditional grayscale method.