Volume 10 Issue 6
Dec.  2017
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XU Hong-lie, KUANG Cheng, ZHANG Le, LI Sha, WANG Shu-jun, TANG Zheng, LI Lin-na. Range limited adaptive brightness preserving multi-threshold histogram equalization algorithm[J]. Chinese Optics, 2017, 10(6): 726-736. doi: 10.3788/CO.20171006.0726
Citation: XU Hong-lie, KUANG Cheng, ZHANG Le, LI Sha, WANG Shu-jun, TANG Zheng, LI Lin-na. Range limited adaptive brightness preserving multi-threshold histogram equalization algorithm[J]. Chinese Optics, 2017, 10(6): 726-736. doi: 10.3788/CO.20171006.0726

Range limited adaptive brightness preserving multi-threshold histogram equalization algorithm

doi: 10.3788/CO.20171006.0726
Funds:

Natural Science Found Project of Jiangsu Province 17KJD140002

Natural Science Project of Taihu University of Wuxi 16WUNS005

More Information
  • Corresponding author: XU Hong-lie, E-mail:xhl1192008@hotmail.com
  • Received Date: 11 Jun 2017
  • Rev Recd Date: 13 Aug 2017
  • Publish Date: 01 Dec 2017
  • In recent years, many histogram equalization algorithms have been proposed for the consumer electronics field. However, many of these algorithms are hard to realize. Even, for example, some algorithms may cause an effect on brightness saturation. Therefore, a range limited adaptive brightness preserving multi-threshold histogram equalization(RLAMHE) algorithm is presented in this paper. First, the input image is smoothed appropriately to obtain the number of its histogram peak points (N+1). Then the Otsu algorithm is extended by the N-threshold, and N segmentation thresholds of the image are obtained in this way, so that the image is segmented according to this threshold. In order to maximize the brightness of the input image, a range of the equalized image is recalculated according to the minimum Absolute Mean Brightness Error(AMBE) criterion of the input and the output image. Finally, all sub-images are equalized separately using the new equalization range. Test results show that the proposed algorithm is more efficient than other algorithms and can obtain sharper image details. Meanwhile, the overall brightness of the image is also ideal. Using this algorithm to process Lena graphs, the absolute mean luminance error is 0.416 4, which is obviously better than that obtained using RLBHE algorithm(0.629 5).

     

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