J4 ›› 2014, Vol. 41 ›› Issue (3): 103-109.doi: 10.3969/j.issn.1001-2400.2014.03.015

• 研究论文 • 上一篇    下一篇



  1. (西安电子科技大学 通信工程学院,陕西 西安  710071)
  • 收稿日期:2013-02-05 出版日期:2014-06-20 发布日期:2014-07-10
  • 通讯作者: 李锦
  • 作者简介:李锦(1987-),女,西安电子科技大学硕士研究生,E-mail:ljathere@163.com.
  • 基金资助:


Novel algorithm for image enhancement with histogram equalization and MSRCR

LI Jin;WANG Junping;WAN Guoting;LI Ziyang;XU Dan;CAO Honghua;ZHANG Guangyan   

  1. (School of Telecommunication Engineering, Xidian Univ., Xi'an  710071, China)
  • Received:2013-02-05 Online:2014-06-20 Published:2014-07-10
  • Contact: LI Jin



关键词: 图像增强, 直方图均衡化, Retinex算法, 图像融合, IC缺陷特征提取


In order to conveniently extract the extra material defect features from an IC real image, this paper proposes a new method for a color image enhancement combined with histogram equalization (HE) and Multi-Scale Retinex with Color Restoration (MSRCR). Using histogram equalization to color image enhancement can significantly improve the contrast but will reduce the original information entropy, the Retinex algorithm can improve the details of the dark area but will lead to the phenomena such as the white and color distortion, low contrast. The new algorithm, according to the characteristics of the processing results of the above two algorithms, weightily fusing the HE enhanced image and the MSRCR enhanced image, has been one of the best weighting factors after a lot of test statistics. Experimental results show that the improved algorithm produces greater enhancement in the image's brightness, contrast, detail, and others and that it not only improves the overall visual effect of the image, but also gives the maximum information entropy. Through objective and subjective evaluation, it is shown that the algorithm has a fantastic effect on enhancement of color image, compared to the HE and MSRCR algorithm that process separately, and that it can better describe IC defects in detail,  which is conducive to the detection and defect feature extraction of the subsequent target, and verify the versatility of the algorithm.

Key words: image enhancement, histogram equalization, Retinex algorithm, image fusion, IC defect feature extraction


  • TP391.4