基于NSST的遥感图像增强算法研究

发布时间：2018-04-20 04:29

本文选题：遥感图像 + 图像增强　；参考：《新疆大学》2017年硕士论文

【摘要】：遥感图像作为人们对地球进行研究和监测的一个重要的依据,起到十分重要的作用。遥感已经从军事领域转向了民用方面,由此使其发展的更加迅速。但是遥感图像在获取和传输的过程中,受到很多因素的影响,如传感器、大气等,使得到的遥感图像变得失真、模糊、对比度低等。为了便于研究人员对遥感图像的识别,就必须对遥感图像进行处理。图像增强是一种图像处理方法,主要是针对图像中的一些有用信息进行突出或强化。因此,图像增强对遥感图像的识别是必不可少的。本文针对遥感图像存在的低对比度、低信噪比、边缘保持较弱、细节丢失等问题,提出了两种新的图像增强算法。其中一种是基于在NSST域的自适应阈值和引导滤波相结合的遥感图像增强算法。引导滤波是一种图像滤波算法,具有良好的平滑能力的同时还能对图像边缘梯度能很好的保持,得到了研究人员的关注。鉴于这些特性,本文将结合引导滤波对图像的细节和边缘部分进行增强。首先,该算法通过对待处理的图像进行NSST分解,将图像分解成为一个低频部分和若干个高频部分。然后,采用线性变换对低频部分进行线性拉伸,目的在于对比度的改变;高频部分,进行抑制噪声处理,将采用自适应阈值法,其次再进行引导滤波增强图像的细节部分和边缘梯度。最后,将处理后低频和高频部分进行重构处理,得到增强后的图像。通过实验表明,该算法对遥感图像的视觉效果得到了改善,客观指标上与对比算法相比,信息熵、峰值信噪比和结构相似度有了一定的提升。本文另外一种是基于NSST域的直方图均衡和引导滤波相结合的遥感图像增强算法。直方图均衡是一种经典的用来提高对比度的算法,本文用它来对图像进行预处理,提高图像整体的对比度。经过NSST分解后的低频部分的处理与上一种算法一样采用线性变换的方式,高频部分的去噪处理采用阈值去噪的方法,对于图像的细节部分和边缘,还是采用引导滤波的方法。实验表明,与对比算法相比较,该算法明显地提升了图像的对比度,增强了图像的细节和边缘梯度能力。
[Abstract]:Remote sensing image plays an important role as an important basis for people to study and monitor the earth. Remote sensing has shifted from military to civilian, thus making its development more rapid. However, in the process of obtaining and transmitting remote sensing image, it is affected by many factors, such as sensor, atmosphere and so on, which make the remote sensing image become distorted, blurred and low contrast. In order to facilitate the recognition of remote sensing images, remote sensing images must be processed. Image enhancement is an image processing method, which is mainly used to highlight or enhance some useful information in the image. Therefore, image enhancement is essential for remote sensing image recognition. In this paper, two new image enhancement algorithms are proposed to solve the problems of low contrast, low signal-to-noise ratio (SNR), weak edge retention and detail loss in remote sensing images. One is a remote sensing image enhancement algorithm based on adaptive threshold and guided filtering in NSST domain. The guided filter is a kind of image filtering algorithm, which has good smoothing ability and can keep the edge gradient of the image well, which has been paid attention to by researchers. In view of these features, this paper will enhance the details and edges of the image in combination with bootstrap filtering. Firstly, the algorithm decomposes the image into a low frequency part and several high frequency parts by NSST decomposition. Then, the low frequency part is stretched linearly by linear transformation to change the contrast, and the high frequency part, which is used to suppress noise, will adopt the adaptive threshold method. Secondly, the detail part and edge gradient of the image are enhanced by guided filtering. Finally, the processed low frequency and high frequency parts are reconstructed to get the enhanced image. The experiments show that the visual effect of the algorithm is improved, and the information entropy, peak signal-to-noise ratio and structural similarity are improved compared with the contrast algorithm. The other one is a remote sensing image enhancement algorithm based on histogram equalization and guided filtering in NSST domain. Histogram equalization is a classical algorithm to improve the contrast. In this paper, we use it to preprocess the image to improve the overall contrast of the image. The processing of the low-frequency part after NSST decomposition is the same as that of the previous algorithm. The high-frequency part is de-noised by the threshold denoising method, and the image details and edges are still processed by the guided filtering method. The experimental results show that compared with the contrast algorithm, the algorithm improves the contrast of the image, and enhances the ability of image detail and edge gradient.
【学位授予单位】：新疆大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP751

【参考文献】