Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (1): 202-205.DOI: 10.11772/j.issn.1001-9081.2015.01.0202

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Remote sensing image enhancement algorithm based on Shearlet transform and multi-scale Retinex

WANG Jingjing1, JIA Zhenhong1, QIN Xizhong1, YANG Jie2, Nikola KASABOV3   

  1. 1. School of Information Science and Engineering, Xinjiang University, Urumqi Xinjiang 830046, China;
    2. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China;
    3. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand
  • Received:2014-08-13 Revised:2014-09-16 Online:2015-01-01 Published:2015-01-26

基于Shearlet变换和多尺度Retinex的遥感图像增强算法

王静静1, 贾振红1, 覃锡忠1, 杨杰2, Nikola KASABOV3   

  1. 1. 新疆大学 信息科学与工程学院, 乌鲁木齐830046;
    2. 上海交通大学 图像处理与模式识别研究所, 上海200240;
    3. 奥克兰理工大学 知识工程与发现研究所, 新西兰 奥克兰1020
  • 通讯作者: 贾振红
  • 作者简介:王静静(1989-),女,新疆乌鲁木齐人,硕士研究生,主要研究方向:图像处理;贾振红(1964-),男,河南洛阳人,教授,博士生导师,主要研究方向:光通信、信号与信息处理;覃锡忠(1964-),男,重庆长寿人,副教授,主要研究方向:信号与信息处理;杨杰(1964-),男,上海人,教授,博士,主要研究方向:图像处理、模式识别;NikolaKasabov(1948-),男,保加利亚索非亚人,教授,博士,主要研究方向:数字图像处理、模式识别.
  • 基金资助:

    教育部促进与美大地区科研合作与高层次人才培养项目(20101595).

Abstract:

Aiming at the problem that the traditional wavelet transform, curverlet transform and contourlet transform are unable to provide the optimal sparse representation of image and can not obtain the better enhancement effect, an image enhancement algorithm based on Shearlet transform was proposed. The image was decomposed into low frequency components and high frequency components by Shearlet transform. Firstly, Multi-Scale Retinex (MSR) was used to enhance the low frequency components of Shearlet decomposition to remove the effect of illumination on image; secondly, the threshold denoising was used to suppress noise at high frequency coefficients of each scale. Finally, the fuzzy contrast enhancement method was used to the reconstruction image to improve the overall contrast of image. The experimental results show that proposed algorithm can significantly improve the image visual effect, and it has more image texture details and anti-noise capabilities. The image definition, the entropy and the Peak Signal-to-Noise Ratio (PSNR) are improved to a certain extent compared with the Histogram Equalization (HE), MSR and Fuzzy contrast enhancement in Non-Subsampled Contourlet Domain (NSCT_fuzzy) algorithms. The operation time reduces to about one half of MSR and one tenth of NSCT_fuzzy.

Key words: Shearlet transform, Multi-Scale Retinex (MSR), low frequency sub-band, high frequency sub-band, fuzzy contrast

摘要:

传统的小波变换、曲波变换和轮廓波变换无法对图像提供最优的稀疏表示,不能取得好的增强效果,为此,提出了一种基于剪切波(Shearlet)变换的图像增强算法.经Shearlet变换,图像被分解成低频分量和高频分量.首先,对Shearlet变换分解后的低频分量进行多尺度Retinex(MSR)调整,以减轻光照条件对图像的影响;其次,对各尺度、各方向上的高频系数采用阈值抑噪来消除噪声;最后,对重构图像进行模糊对比度增强,提高图像的整体对比度.实验结果表明该算法能够明显改善图像的视觉效果,突出图像的纹理细节且具有良好的抗噪性能.与直方图均衡(HE)、MSR、基于非下采样轮廓波变换(NSCT)的图像模糊增强(NSCT_fuzzy)算法相比,图像清晰度、信息熵、峰值信噪比(PSNR)均有一定的提高,且运行时间缩短为MSR的1/2和NSCT_fuzzy的1/10左右.

关键词: Shearlet变换, 多尺度Retinex, 低频子带, 高频子带, 模糊对比度

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