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
针对部分遥感图像整体亮度偏暗、对比度较低的特点，为提高遥感图像的视觉效果和可解译性，提出了一种基于非下采样Shearlet变换（non-subsampled shearlet transform，NSST）和参数化对数图像处理（parameterized logariihmic image processing，PLIP）模型的遥感图像增强方法。首先，遥感图像经非下采样Shearlet变换分解成低频分量和高频分量；然后依据PLIP模型对其低频分量进行增强，提高图像的对比度；同时利用改进的模糊增强方法对高频分量进行增强，突显边缘细节信息。大量试验结果表明，与双向直方图均衡增强、基于平稳小波变换的...
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| Published in | 测绘学报 Vol. 44; no. 8; pp. 884 - 892 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
江西省数字国土重点实验室，江西 南昌 330013
2015
国土资源部地质信息技术重点实验室，北京 100037 兰州大学甘肃省西部矿产资源重点实验室，甘肃 兰州 730000 中国地质科学院矿产资源研究所国土资源部成矿作用与资源评价重点实验室，北京 100037 南京航空航天大学电子信息工程学院,江苏 南京,210016%南京航空航天大学电子信息工程学院，江苏 南京 210016 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-1595 |
| DOI | 10.11947/j.AGCS.2015.20140466 |
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| Abstract | 针对部分遥感图像整体亮度偏暗、对比度较低的特点，为提高遥感图像的视觉效果和可解译性，提出了一种基于非下采样Shearlet变换（non-subsampled shearlet transform，NSST）和参数化对数图像处理（parameterized logariihmic image processing，PLIP）模型的遥感图像增强方法。首先，遥感图像经非下采样Shearlet变换分解成低频分量和高频分量；然后依据PLIP模型对其低频分量进行增强，提高图像的对比度；同时利用改进的模糊增强方法对高频分量进行增强，突显边缘细节信息。大量试验结果表明，与双向直方图均衡增强、基于平稳小波变换的增强、基于非下采样Contourlet变换的增强等5种图像增强方法相比，本文提出的方法在主观视觉效果和对比度、清晰度等客观定量评价指标两个方面均有优势，能更有效地提高遥感图像的对比度、增强边缘纹理细节信息，视觉效果更佳。 |
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| AbstractList | 针对部分遥感图像整体亮度偏暗、对比度较低的特点，为提高遥感图像的视觉效果和可解译性，提出了一种基于非下采样Shearlet变换（non-subsampled shearlet transform，NSST）和参数化对数图像处理（parameterized logariihmic image processing，PLIP）模型的遥感图像增强方法。首先，遥感图像经非下采样Shearlet变换分解成低频分量和高频分量；然后依据PLIP模型对其低频分量进行增强，提高图像的对比度；同时利用改进的模糊增强方法对高频分量进行增强，突显边缘细节信息。大量试验结果表明，与双向直方图均衡增强、基于平稳小波变换的增强、基于非下采样Contourlet变换的增强等5种图像增强方法相比，本文提出的方法在主观视觉效果和对比度、清晰度等客观定量评价指标两个方面均有优势，能更有效地提高遥感图像的对比度、增强边缘纹理细节信息，视觉效果更佳。 P237; 针对部分遥感图像整体亮度偏暗、对比度较低的特点,为提高遥感图像的视觉效果和可解译性,提出了一种基于非下采样 Shearlet 变换(non-subsampled shearlet transform,NSST)和参数化对数图像处理(parameterized logarithmic image processing,PLIP)模型的遥感图像增强方法。首先,遥感图像经非下采样 Shearlet 变换分解成低频分量和高频分量；然后依据 PLIP 模型对其低频分量进行增强,提高图像的对比度；同时利用改进的模糊增强方法对高频分量进行增强,突显边缘细节信息。大量试验结果表明,与双向直方图均衡增强、基于平稳小波变换的增强、基于非下采样 Contourlet 变换的增强等5种图像增强方法相比,本文提出的方法在主观视觉效果和对比度、清晰度等客观定量评价指标两个方面均有优势,能更有效地提高遥感图像的对比度、增强边缘纹理细节信息,视觉效果更佳。 |
| Abstract_FL | Aiming at parts of remote sensing images with dark brightness and low contrast,a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpret-ability of remote sensing images.Firstly,a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP (parameterized logarithmic image processing)model,which can improve the contrast of image,whi le the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details.A large number of experimental results show that,compared with five kinds of image enhancement methods such as bidirectional histogram equalization method,the method based on stationary wavelet transform and the method based on non-subsampled contourlet transform,the proposed method has advantages in both subjective visual effects and objective quantitative evaluation indexes such as contrast and definition, which can more effectively improve the contrast of remote sensing image and enhance edges and texture details with better visual effects. |
| Author | 陶飞翔 吴一全 |
| AuthorAffiliation | 南京航空航天大学电子信息工程学院,江苏南京210016 国土资源部地质信息技术重点实验室,北京100037 兰州大学甘肃省西部矿产资源重点实验室,甘肃兰州730000 江西省数字国土重点实验室,江西南昌330013 中国地质科学院矿产资源研究所国土资源部成矿作用与资源评价重点实验室,北京100037 |
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| Author_FL | TAO Feixiang WU Yiquan |
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| DocumentTitleAlternate | Remote Sensing Image Enhancement Based on Non-subsampled Shearlet Transform and Parameterized Logarithmic Image Processing Model |
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| Keywords | fuzzy enhancement 遥感图像 非下采样 Shearlet 变换(NSST) image enhancement non-subsampled Shearlet transform (NSST) 图像增强 模糊增强 parameters logarithmic image processing (PLIP)model remote sensing image 参数化对数图像处理(PLIP)模型 |
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| Notes | 11-2089/P Aiming at parts of remote sensing images with dark brightness and low contrast, a remote sensing image enhancement method based on non-subsampled Shearlet transform and parameterized logarithmic image processing model is proposed in this paper to improve the visual effects and interpret- ability of remote sensing images. Firstly, a remote sensing image is decomposed into a low-frequency component and high frequency components by non-subsampled Shearlet transform.Then the low frequency component is enhanced according to PLIP （parameterized logarithmic image processing） model, which can improve the contrast of image, while the improved fuzzy enhancement method is used to enhance the high frequency components in order to highlight the information of edges and details. A large number of experimental results show that, compared with five kinds of image enhancement methods such as bidirectional histogram equalization method, the method based on stationary wavelet transform and the method based on non-subsa |
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| Publisher | 江西省数字国土重点实验室，江西 南昌 330013 国土资源部地质信息技术重点实验室，北京 100037 兰州大学甘肃省西部矿产资源重点实验室，甘肃 兰州 730000 中国地质科学院矿产资源研究所国土资源部成矿作用与资源评价重点实验室，北京 100037 南京航空航天大学电子信息工程学院,江苏 南京,210016%南京航空航天大学电子信息工程学院，江苏 南京 210016 |
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| Snippet | 针对部分遥感图像整体亮度偏暗、对比度较低的特点，为提高遥感图像的视觉效果和可解译性，提出了一种基于非下采样Shearlet变换（non-subsampled shearlet... P237; 针对部分遥感图像整体亮度偏暗、对比度较低的特点,为提高遥感图像的视觉效果和可解译性,提出了一种基于非下采样 Shearlet 变换(non-subsampled shearlet... |
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| SubjectTerms | rlet变换（NSST） 参数化对数图像处理（PLIP）模型 图像增强 增强 模糊 遥感图像 非下采样Shea |
| Title | 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 |
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