基于字典学习与稀疏表示的灰度图像颜色重建算法

Color Reconstruction Algorithm for Grayscale Images Based on Dictionary Learning and Sparse Representation

为了充分利用参考彩色图像与待处理灰度图像的关联关系,进一步提高图像颜色重建的自动化程度,利用稀疏表示理论和字典学习方法,提出一种自动全局图像着色算法.首先利用图像亮度、特征信息、图像颜色信息之间的相关性,依据参考图像训练出一个亮度-特征-颜色的联合字典;然后利用目标灰度图像的亮度和特征信息计算出其在该字典下的稀疏表示系数;最后利用上述联合字典与计算得到的稀疏表示系数进行灰度图像的颜色信息重建.文中算法无需进行图像分割,针对整幅图像进行着色,是一种自动的全局算法.实验结果表明,该算法可以有效地对灰度图像进行着色,对于色调单一的图像,着色效果更好.

In order to take full advantage of the relationship between reference color images and the objective grayscale image and to improve the degree of automation for image color reconstruction, we presented an automatic algorithm for image colorization based on dictionary learning and sparse representation. Firstly, a joint dictionary is trained by reference color images according to the correlations among the luminance, feature and color of trained images. And then the sparse coefficients under the joint dictionary for the objective grayscale image are computed by using its luminance and feature information. Finally, the color information is reconstructed using the above joint dictionary and the obtained sparse coefficients. Image segmentation is not necessary in the proposed algorithm. The color reconstruction is made on the entire image and therefore the proposed algorithm is global and automatic. Experimental results demonstrate that the algorithm presented in this paper is effective and efficient, espec