基于非下采样剪切波变换与引导滤波结合的遥感图像增强

doi:10.11772/j.issn.1001-9081.2016.10.2880

计算机应用 ›› 2016, Vol. 36 ›› Issue (10): 2880-2884.DOI: 10.11772/j.issn.1001-9081.2016.10.2880

基于非下采样剪切波变换与引导滤波结合的遥感图像增强

吕笃良¹, 贾振红¹, 杨杰², Nikola KASABOV³

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

收稿日期:2016-04-27 修回日期:2016-06-28 发布日期:2016-10-10
通讯作者: 贾振红,E-mail:jzhh@xju.edu.cn
作者简介:吕笃良(1991—),男,河北吴桥人,硕士研究生,主要研究方向:图像增强;贾振红(1964—),男,河南洛阳人,教授,博士,主要研究方向:光通信、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（DICE2014-2029）。

Remote sensing image enhancement based on combination of non-subsampled shearlet transform and guided filtering

LYU Duliang¹, JIA Zhenhong¹, YANG Jie², Nikola KASABOV³

1. College of Information Science and Engineering, Xinjiang University, Urumqi Xinjiang 830046, China;
2. Institute of Image Processing and Pattern Recognition, Shanghai JiaoTong University, Shanghai 200240, China;
3. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand

Received:2016-04-27 Revised:2016-06-28 Published:2016-10-10
Supported by:
BackgroundThis work is partially supported by the International Cooperative Research and Personnel Training Projects of the Ministry of Education of China (DICE2014-2029).

摘要/Abstract

摘要： 针对遥感图像中对比度低、细节信息缺失和边缘梯度保持能力较弱等问题，提出了一种基于非下采样剪切波变换（NSST）与引导滤波相结合的遥感图像增强算法。首先，原始图像通过NSST被分解成低频子带和高频子带两部分。然后，对低频子带进行线性增强，提高整体对比度；采用自适应阈值法抑制高频子带的噪声，再对去噪后的高频子带进行引导滤波增强，提高图像的细节信息和边缘梯度保持能力。最后，对两部分子带进行NSST反变换，得到增强后的图像。实验结果表明，与直方图均衡、基于Contourlet变换和模糊理论的图像增强算法、基于非下采样Contourlet变换与反锐化掩膜结合的遥感图像增强算法以及基于非下采样Shearlet变换与参数化对数图像处理相结合的遥感图像增强算法相比，该算法的图像信息熵、峰值信噪比（PSNR）和结构相似性（SSIM）都有一定的提升，能明显地改善图像视觉效果，使得图像纹理更加清晰。

关键词: 遥感图像, 图像增强, 非下采样Shearlet变换, 自适应阈值去噪, 引导滤波

Abstract: Aiming at the problem of low contrast, lack of the details and weakness of edge gradient retention in remote sensing images, a new remote sensing image enhancement method based on the combination of Non-Subsampled Shearlet Transform (NSST) and guided filtering was proposed. Firstly, the input image was decomposed into a low-frequency component and several high-frequency components by NSST. Then a linear stretch was adopted for the low-frequency component to improve the overall contrast of the image, and the adaptive threshold method was used to restrain the noise in the high-frequency components. After denoising, the high-frequency components were enhanced by guided filtering to improve the detail information and edge-gradient retention ability. Finally, the final enhanced image was reconstructed by applying the inverse NSST to the processed low-frequency and high-frequency components. Experimental results show that, compared with the Histogram Equalization (HE), image enhancement based on contourlet transform and fuzzy theory, remote sensing image enhancement based on nonsubsampled contourlet transform and unsharp masking as well as remote sensing image enhancement based on non-subsampled shearlet transform and parameterized logarithmic image processing, the proposed method can effectively increase the information entropy, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM), which can obviously improve the visual effect of the image and make the texture of the image more clear.

Key words: remote sensing image, image enhancement, Non-Subsampled Shearlet Transform (NSST), adaptive threshold denoising, guided filtering

中图分类号:

吕笃良, 贾振红, 杨杰, Nikola KASABOV. 基于非下采样剪切波变换与引导滤波结合的遥感图像增强[J]. 计算机应用, 2016, 36(10): 2880-2884.

LYU Duliang, JIA Zhenhong, YANG Jie, Nikola KASABOV. Remote sensing image enhancement based on combination of non-subsampled shearlet transform and guided filtering[J]. Journal of Computer Applications, 2016, 36(10): 2880-2884.

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基于非下采样剪切波变换与引导滤波结合的遥感图像增强

Remote sensing image enhancement based on combination of non-subsampled shearlet transform and guided filtering

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