A Lightweight Image Decolorization Approach based on Contrast Preservation

Nowadays, color-to-gray conversion known as de-colorization is widely used not only in image processing but also in machine learning and deep learning-based applications, due to its lower complexity. In the conventional decolorization process, in general, fixed conversion coefficients are used for whole images. Using fixed coefficients, namely weighting parameters, for the whole images may lead to deterioration of the quality of original color images. On the other hand, using unfixed conversion coefficients, which depend on the contents of the images, demonstrates better decolorization performance with respect to the fixed coefficients. The critical points in the decolorization process are to preserve spatial information, such as contrast preserving, and to have less computational complexity. In this study, a very fast decolorization method is proposed without sacrificing contrast preservation. The proposed method exploits the gradient-based correlation similarity approach, in which global and local contrast information is considered for a total of 66 distinct weighting coefficients. Under the CADIK dataset, COLOR250 dataset, and high-resolution images, these weighting coefficients are reduced by using the frequency of best weighting coefficients. Experimental results show that the proposed method can be used in real-time without compromising the image decolorization performance.