Colorizing grayscale images so that the resulting image appears natural is a hard problem. Previous colorization algorithms generally use just the luminance information and ignore the rich texture information, which means that regions with the same luminance but different textures may mistakenly be assigned the same color. A novel automatic texture-map-based grayscale image colorization method is proposed. The texture map is generated with bilateral decomposition and a Gaussian high pass filter, which is further optimized using statistical adaptive gamma correction method. The segmentation of the spatial map is performed using locally weighted linear regression on its histogram in order to match the grayscale image and the source image. Within each of the spatial segmentation, a weighted color-luminance correspondence is achieved by the results of locally weighted linear regression. The luminance-color correspondence between the grayscale image and the source image can thus be used to colorize the grayscale image directly. By considering the consistency of both color information and texture information between two images, various plausible colorization results are generated using this new method.