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A fast implementation of Adaptive Histogram Equalization is given in this paper

A Fast Implementation Of Adaptive Histogram Equalization

2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4, no. null (2006): 1330-+

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摘要

Adaptive Histogram Equalization (AHE) is a popular and effective algorithm for image contrast enhancement. But it's quite computationally expensive and time consuming. In this paper, a fast implementation of AHE based on pure software techniques is proposed Three accelerative techniques are combined to form the new fast AHE: First, local ...更多

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简介
  • Adaptive Histogram Equalization (AHE) is a popular and effective algorithm for image contrast enhancement.
  • From Algorithm 1, the authors could find that AHE is quite computationally expensive
  • For every pixel, it need W2 additions to get the local histogram, and l additions for CHistl, one multiplication and one division to map the origin grey level to new one.
重点内容
  • Adaptive Histogram Equalization (AHE) is a popular and effective algorithm for image contrast enhancement
  • Since local histogram equalization must be performed for every pixel in the entire image, the computation complexity is very high
  • (2) In AHE, the new grey level is depending on the cumulative histogram function value in origin grey level
  • As we can see from the table, when W=128, compare with traditional AHE, our fast adaptive histogram equalization (FAHE) algorithm save about 98.1% and 97.7% processing time for 8bits and 10bits image respectively
  • A fast implementation of AHE is given in this paper
  • Three pure software techniques are adopted to improve the speed of AHE
结果
  • The authors have tested the FAHE on one 476*594 8bits medical image and one 364*1180 10bits industrial Xray image.
  • Fig. 2 shows the medical image and processed result (By theoretical analysis the authors know the results are just the same as original AHE).
  • As the authors can see from the table, when W=128, compare with traditional AHE, the FAHE algorithm save about 98.1% and 97.7% processing time for 8bits and 10bits image respectively.
  • Even compare with IAHE, the algorithm save 12.2% and 21.6% time respectively
结论
  • A fast implementation of AHE is given in this paper.
  • Three pure software techniques are adopted to improve the speed of AHE.
  • Theoretical analysis and experimental results show that it is quite effective.
  • It can be realized in nearly real-time on general PC, which is important for medical image processing.
  • The techniques the authors used in FAHE could be used in other pixel based histogram equalization algorithm with fixed block size, such as CLAHE (Contrast Limiting Adaptive Histogram Equalization) [1], CLHE(Constrained Local Histogram Equalization) [7], MAHE (Multi-scale Adaptive Histogram Equalization) [8], and some new various local histogram equalization algorithms[9], etc
表格
  • Table1: Pixel computation complexity comparison
  • Table2: Average time (ms) for 8bits medical image
  • Table3: Average time (ms) for 10bits industrial X-ray image
Download tables as Excel
引用论文
  • S.M Pizer, E.P. Amburn, J.D. Austin, et al. Adaptive Histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 1987, 39(3): 355~368
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  • C.W. Kurak Jr. Adaptive histogram equalization: a parallel implementation. Fourth Annual IEEE Symposium on Computer-Based Medical Systems, 1991, 192~199
    Google ScholarLocate open access versionFindings
  • Z. Salcic, J. Sivaswamy. IMECO: A Reconfigurable FPGA-based Image Enhancement Co-Processor Framework. Real-Time Imaging, 1999, 5(6): 385~395.
    Google ScholarLocate open access versionFindings
  • J. Y. Kim, L. S. Kim, S. H. Hwang. An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(4): 475~484
    Google ScholarLocate open access versionFindings
  • T. Gillespy. Optimized algorithm for adaptive histogram equalization. Conference: Medical Imaging, Feb 1998, San Diego, CA, USA
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  • T. K. Kim, J. K. Paik, B. S. Kang. Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Transactions on Consumer Electronics, 1998, 44(1): 82~87
    Google ScholarLocate open access versionFindings
  • Z. H. Chan, H. Y. Francis, F. K. Lam. Image Contrast Enhancement by Constrained Local Histogram Equalization. Computer Vision and Image Understanding, 1999, 73(2): 281~290
    Google ScholarLocate open access versionFindings
  • Y. P. Jin, L. Fayad, A. Laine. Contrast enhancement by multi-scale adaptive histogram equalization. Proceedings of SPIE - The International Society for Optical Engineering, 2001, 4478: 206~213
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  • M. Eramian, D. Mould. Histogram equalization using neighborhood metrics, Proceedings. The 2nd Canadian Conference on Computer and Robot Vision, 2005, 397~404
    Google ScholarLocate open access versionFindings
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