Spatial constrained clustering analysis based specular highlight removal for component image
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TN911. 73

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    Abstract:

    To solve the problem of image quality degradation caused by specular highlights, a specular highlight removal method based on spatial constrained clustering analysis is proposed in this paper. Firstly, after projecting the component image into the minimummaximum chromaticity space, the fixed clustering center is introduced to realize the separation of chromatic pixels and achromatic pixels while ensuring the similar chromaticity in a cluster. Then, the intensity ratio adjustment and brightness histogram statistics are used to determine the specular reflection components in the clustering of chromatic pixels and achromatic pixels respectively. Finally, combined with dichromatic reflection model, specular highlight removal is realized. Experimental results show that the entropy value and structure similarity of the image are 5. 750 and 0. 998 8 after highlight removal by proposed method. The proposed method can effectively remove specular highlights in chromatic and achromatic regions and obtain high quality images.

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  • Received:
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  • Online: March 06,2023
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