摘要
Due to the different lighting environments or other reasons, the pixel colors may be quite different in one image which causes distinct visual discontinuities. It makes the analysis and processing of such an image more difficult and sometime impossible. In this paper, a unified multi-toning image adjustment method is proposed to solve this problem. First, a novel unsupervised clustering method was proposed to partition the source and the target image into a certain number of subsets with similar color statistics. By matching the texture characteristics and luminance distribution between the blocks, it can create optimized correspondence. Then, the color information was transferred from the matched pixels in the source blocks to the target ones. Graph cut method was used to optimize the seams between different subsets in the final step. This method can automatically perform color adjustment of a multi-toning image. It is simple and efficient. Various results show the validity of this method.
Due to the different lighting environments or other reasons, the pixel colors may be quite different in one image which causes distinct visual discontinuities. It makes the analysis and processing of such an image more difficult and sometime impossible. In this paper, a unified multi-toning image adjustment method is proposed to solve this problem. First, a novel unsupervised clustering method was proposed to partition the source and the target image into a certain number of subsets with similar color statistics. By matching the texture characteristics and luminance distribution between the blocks, it can create optimized correspondence. Then, the color information was transferred from the matched pixels in the source blocks to the target ones. Graph cut method was used to optimize the seams between different subsets in the final step. This method can automatically perform color adjustment of a multi-toning image. It is simple and efficient. Various results show the validity of this method.
基金
Supported by Natural Science Foundation of China （61170118 and 60803047）, the Specialized Research Fund for the Doctoral Program of Higher Education of China （200800561045）
