基于小波变换的图像阈值去噪法  

Image denoising method based on wavelet transformation

作　　者：项芳莉[1] 

机构地区：[1]黄山学院机电与信息工程学院,安徽黄山245041

出　　处：《淮南师范学院学报》2014年第3期12-15,共4页Journal of Huainan Normal University

摘　　要：图像去噪是图像处理领域中的重要研究方向之一。小波变换具有选基灵活和多分辨率特性等,能清晰图像,因此在图像去噪中获得了广泛的应用。当含噪图像经过小波变换后,图像和噪声在不同的分辨率下呈现出不同规律,设定阈值门限,接着调整小波系数,最终达到去噪的目的。在硬软阈值去噪法的基础上提出新算法,并且利用Matlab进行仿真,实验结果表明新算法的峰值信噪比较高,具有较好的去噪效果。Denoising is an important research in the field of image processing. Wavelet transformation has the following favorable characteristics： Selected based flexibility and multi-resolution features, and making images clearer, so it is widely used in denoising. When the model is re-processed by wavelet transformation, the image and noise appear with different regular patterns of different layers of wavelet decomposition coefficients.After setting threshold and adjusting the wavelet coefficients, we can remove the noise in the image.This article suggests a new algorithm on the basis of the hard and soft threshold denoising method. The results show that the wavelet can get higher PSNR for the denoised image and good de-noising effect.

关 键 词：小波变换 小波去噪 阈值 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]