基于小波阈值法的矿山遥感图像非局部均值去噪

发布时间：2019-04-11 13:56

【摘要】：矿山遥感图像在获取、压缩、传输、解码的过程中易混入大量随机噪声,导致图像清晰度较低,难以直接进行分析研究。为此,将小波变换与非局部均值滤波算法(Non-local means filtering algorithm,NLM)相结合提出了一种基于小波阈值法的矿山遥感图像非局部均值去噪算法。该算法首先结合小波硬阈值、软阈值去噪模型以及现有的改进型小波阈值去噪模型的特点,建立一种改进型小波阈值去噪模型并用于去除遥感图像中的随机噪声;然后将原始遥感图像与小波去噪后的图像作差运算,得到原始差值图像,再对原始差值图像进行非局部均值滤波,得到滤波后的差值图像;最后将小波去噪后的图像与滤波后的差值图像进行融合。采用MATLAB语言编写程序,试验数据为白云鄂博矿区的2幅遥感图像,采用峰值信噪比(Peak signal noise to ratio,PSNR)和算法耗时等指标对算法去噪效果进行评价。试验结果表明:所提算法的去噪结果明显优于小波软阈值去噪模型及非局部均值滤波,此外,该算法耗时相对于其余2类算法而言也有一定的优势,对于提高矿山遥感图像的判读精度有一定的参考价值。
[Abstract]:In the process of acquisition, compression, transmission and decoding of mine remote sensing images, it is easy to mix in a large number of random noises, which leads to the low definition of the images, which makes it difficult to analyze and study them directly. In this paper, a non-local mean denoising algorithm based on wavelet threshold method for mine remote sensing images is proposed by combining wavelet transform with non-local mean filtering (Non-local means filtering algorithm,NLM) algorithm. Firstly, combining the characteristics of wavelet hard threshold, soft threshold de-noising model and existing improved wavelet threshold denoising model, an improved wavelet threshold de-noising model is established and used to remove random noise in remote sensing image. Then the difference operation between the original remote sensing image and the wavelet denoised image is done to get the original difference image, and then the non-local mean filtering of the original difference image is carried out to get the filtered difference image. Finally, the wavelet denoised image is fused with the filtered difference image. Two remote sensing images from Baiyun Obo mining area were tested with MATLAB language. The peak signal-to-noise ratio (Peak signal noise to ratio,PSNR) and the time consuming of the algorithm were used to evaluate the denoising effect of the algorithm. The experimental results show that the proposed algorithm is superior to the wavelet soft threshold de-noising model and non-local mean filtering. In addition, the proposed algorithm has some advantages over the other two kinds of algorithms in terms of time-consuming. It has certain reference value for improving the interpretation precision of mine remote sensing image.
【作者单位】：唐山广播电视大学远程教育中心;唐山师范学院继续教育学院;
【分类号】：TP751

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