In theory

color-to-gray transformation is a process of dimensionality reduction

thus making information loss inevitable. Therefore

the goal of decolorization is to use the limited gray level range to preserve as much information of the original color image as possible. Researchers have proposed many related algorithms. However

the algorithms fail in the simultaneous preservation of the local and global contrast

as well as the contrast

color consistency

and grayscale pixel

of the original image. To solve these problems

we propose a new approach that can maximally maintain the features of the original color image. To preserve the structure and local information

we use a bimodal Gaussian distribution

followed by the difference between the pixel and its neighbors

to construct the error energy function. For global color consistency

we use locally linear embedding to build the energy function

which causes the same color pixels to exhibit the same gray levels in the result. For grayscale feature preservation

we distinguish grayscale pixels and specify that the gray values of grayscale pixels are known quantities and are initially unchanged during conversion. We then construct the energy function between grayscale pixels and other pixels. Thereafter

we build the objective function with the linear combination of the three energy functions and the gray image is obtained by solving the objective function using the iterative method. Experimental results show that our algorithm can preserve salient structure and fine detail

as well ensure that the same color can be transformed to the same grayscale and that the gray value of the grayscale pixel would be unchanged after conversion. Our algorithm conforms to the degree of perception about contrast change in image and can preserve detailed information and global structure. The algorithm can be applied to digital printing

pattern recognition

and so on

and thus has great application value.