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Adaptive tone-preserved image detail enhancement

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Abstract

An adaptive tone-preserved algorithm for image detail enhancement is proposed to retain the tonal distribution of the input image and avoid experiential manipulation. First of all, domain transform based multi-scale image decomposition is carried out to quickly divide the input image into a base image which contains the coarse-scale image information, and the detail layers which contain the fine-scale details. Then, during the process of detail enhancement and synthesis, we construct an adaptive detail enhancement function based on the edge response, to prevent the exaggeration of strong edges and increase the enhancing magnitude of small details. Finally, in order to keep the color values of the input image and the gradient values of the detail enhanced image, a tonal correction algorithm based on energy optimization is presented to eliminate the distinct tonal differences of the enhanced image from the input image. Our experimental results show that tone-consistent image detail enhancement effect is available for arbitrary input images with unified parameters setting, which is superior to the state-of-the-art methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61003188 and No. 61170098, the State Key Laboratory of Virtual Reality Technology and Systems in Beihang University under Grant No. BUAA-VR-11KF-201107051, the Education Department of Zhejiang Province under Grant No. Y201018011, the National Grand Foundation Research 973 Program of China under Grant No. 2009CB320800, the Key Technology Innovation Team Building Program of Zhejiang Province under Grant No. 2010R50041, and the Zhejiang Provincial Natural Science Foundation of China under Grant No. Y1111159, No. Z1101243 and No. Z1111051.

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Authors and Affiliations

  1. School of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China

    Yun Ling, Caiping Yan, Chunxiao Liu, Xun Wang & Hong Li

  2. State Key Lab of Virtual Reality Technology and Systems, Beihang University, Beijing, 10091, China

    Chunxiao Liu

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  1. Yun Ling
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  2. Caiping Yan
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  3. Chunxiao Liu
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  4. Xun Wang
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  5. Hong Li
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Correspondence to Chunxiao Liu.

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Ling, Y., Yan, C., Liu, C. et al. Adaptive tone-preserved image detail enhancement. Vis Comput 28, 733–742 (2012). https://doi.org/10.1007/s00371-012-0691-2

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