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Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

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Abstract

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.

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Acknowledgements

This work was supported by the National Natural Science Foundations of China (Grant numbers: 61727802 and 61501235) and the Fundamental Research Funds for the Central Universities (Grant numbers: 30916011320). On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, 210094, China

    Dan Yan, Lianfa Bai, Yi Zhang & Jing Han

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  1. Dan Yan
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  2. Lianfa Bai
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  3. Yi Zhang
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  4. Jing Han
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Correspondence to Jing Han.

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Yan, D., Bai, L., Zhang, Y. et al. Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement. Opt Rev 25, 78–93 (2018). https://doi.org/10.1007/s10043-017-0398-8

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