Acta Optica Sinica, Volume. 34, Issue 10, 1033001(2014)

Research on Image Reconstruction of Traditional Chinese Painting Art Based on Radiance Information Matching

Qin Feng*, Yang Weiping, Yang Jia, Li Hongning, Luo Yanlin, Lü Xiankui, Liu Yichao, and Zou Jiping
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  • [in Chinese]
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    Reflection parameters and characteristics of traditional Chinese painting samples are measured and analyzed. A method used to reconstruct traditional Chinese painting image based on radiance information matching is put forward to combine with the colorimetry theory. Hue saturation intensity (HSI) color space is used to separate traditional Chinese painting information of luminance and chroma, and the radiance values which are measured by PR-715 under different illuminating angles are fitted depending on Gaussian function. Thereby the relationship of spatial function between radiance and illuminating angles is built. Images which are shot under 90° illuminating angle are chosen to be the referential color pieces. The images of traditional Chinese painting color pieces can be reconstructed by combining the information of radiance in imaging HSI color space and functional expression of spatial fitting radiance. The results are compared with the real pictures which are shot by Canon EOS 400D single lens reflex camera. Results indicate that images of traditional Chinese painting color pieces can be reconstructed accurately depending on radiance information matching. This method has high feasibility and reliability, as well as important practical application value for reconstruction and reproduction of the digital cultural heritage for painting art.

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    Qin Feng, Yang Weiping, Yang Jia, Li Hongning, Luo Yanlin, Lü Xiankui, Liu Yichao, Zou Jiping. Research on Image Reconstruction of Traditional Chinese Painting Art Based on Radiance Information Matching[J]. Acta Optica Sinica, 2014, 34(10): 1033001

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    Paper Information

    Category: Vision, Color, and Visual Optics

    Received: Feb. 25, 2014

    Accepted: --

    Published Online: Sep. 9, 2014

    The Author Email: Feng Qin (wolfmancool@126.com)

    DOI:10.3788/aos201434.1033001

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