基于剪切波和全变分的农田遥感图像去噪去伪影方法

农田遥感图像在采集过程中会受到噪声影响,为得到准确的农田遥感图像数据,应对获取的农田遥感图像进行去噪预处理.农田遥感图像中的纹理承载了重要信息,在图像降噪的同时保持或增强图像纹理具有重要意义.由于纹理和噪声一样,在频域表现为高频信号,以分解和重构算法为基础的常见滤波(含小波变换)方法在降噪的同时,也会造成纹理清晰度的下降.该文结合农田遥感图像纹理呈现出来的直线特性,将剪切波(Shearlet)和变分理论相结合,提出了一种新的遥感农田图像保纹理降噪方法.该方法首先对较大的遥感图像分块进行shearlet变换,在降噪的同时识别不同图块图像的纹理含量;对细小纹理含量较少的平滑区域,采用保边降噪变分模...

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Published in农业工程学报 Vol. 33; no. z1; pp. 274 - 280
Main Author 梅树立 李晓飞 赵海英 李丽 郭书君
Format Journal Article
LanguageChinese
Published 中国农业大学信息与电气工程学院,北京,100083%北京邮电大学世纪学院移动媒体与文化计算北京市重点实验室,北京,102613 2017
Subjects
图像处理;算法;遥感;去伪影;多分辨率分析;shearlet变换;全变分模型
image processing
algorithms
multi-resolution
去伪影
多分辨率分析
全变分模型
remote sensing
Shearlet transform
遥感
算法
图像处理
shearlet变换
removing artifact
total variation model
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Summary:农田遥感图像在采集过程中会受到噪声影响,为得到准确的农田遥感图像数据,应对获取的农田遥感图像进行去噪预处理.农田遥感图像中的纹理承载了重要信息,在图像降噪的同时保持或增强图像纹理具有重要意义.由于纹理和噪声一样,在频域表现为高频信号,以分解和重构算法为基础的常见滤波(含小波变换)方法在降噪的同时,也会造成纹理清晰度的下降.该文结合农田遥感图像纹理呈现出来的直线特性,将剪切波(Shearlet)和变分理论相结合,提出了一种新的遥感农田图像保纹理降噪方法.该方法首先对较大的遥感图像分块进行shearlet变换,在降噪的同时识别不同图块图像的纹理含量;对细小纹理含量较少的平滑区域,采用保边降噪变分模型去除shearlet变换带来的人工伪影.为避免子图块边界带来的边界效应,该文基于中心仿射变换理论提出了一种新的图像延拓方法,有效提高了图像降噪的效果.试验结果表明,该文算法去噪后的峰值信噪比(peak signal to noise ratio,PSNR)平均值比全变分模型去噪算法大1 dB,该文算法去噪后的PSNR平均比曲线波去噪算法大2 dB.同基于Symmlet小波的Shearlet算法相比,该文算法处理后农田遥感图像中伪影减少,在高斯噪声标准偏差σ为10、20和30 dB时,峰值信噪比PSNR分别提高了13.99%、9.69%和7.75%.
Bibliography:11-2047/S
image processing;algorithms;remote sensing;removing artifact;multi-resolution;Shearlet transform;total variation model
The research on crop phenotype is one of the important measures in crop breeding. Crop breeding is a technology on selecting the good seeds by the crop phenotype. The farm remote sensing image analysis is a simple and effective method for rapid analysis of crop phenotype. However, the farm remote sensing image acquired by UAV (unmanned aerial vehicle) will be affected by the noise. In order to process and analyze the remote sensing image accurately, the remote sensing image should be first denoised. Typical image denoising algorithm of frequency domain is wavelet threshold algorithm. Wavelet transform can identify the singular signal accurately; however, the traditional wavelet can't effectively deal with multidimensional signal. For example, two-dimensional wavelet obtained from one-dimensional wavelet tensor only has horizontal and vertical directions, and the wavelet filter is
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2017.z1.041