作　　者： ; ; ;

机构地区： 中国农业大学信息与电气工程学院

出　　处： 《农业机械学报》 2016年第S1期449-456,共8页

摘　　要： 蝗虫显微切片图像纹理边缘细节丰富,在图像获取、增强等预处理过程中经常会受到外部噪声的干扰,因此针对蝗虫切片图像去噪同时并保留纹理边缘细节的探索是研究不同蝗虫种类细胞构造的基础。基于张量的传统二维小波因其滤波器各向同性,只能表示水平和垂直两个方向,在去噪的同时会把图像中边缘纹理等细节模糊,而剪切波构造的滤波器各向异性,能够表示多个方向,这些优点使得剪切波可以有效地处理高维数据,在逼近奇异曲线时达到最优逼近。本文提出的基于Meyer窗函数的剪切波算法可以识别出图像边缘和纹理,并在去噪的同时保留纹理,以Meyer小波作为剪切波基函数,利用Meyer小波函数和尺度函数构造窗函数,然后采用Meyer窗函数来建立各向异性剪切波滤波器,再利用该剪切波滤波器对蝗虫切片图像进行多尺度分析,经过剪切波变换获得剪切波系数,最后应用硬阈值方法去除蝗虫切片图像噪声系数,经过逆变换得到蝗虫切片去噪图像。采用经典图像质量评价指标均方误差(MSE)、峰值性噪比(PSNR)、结构相似度(SSIM)对本文算法去噪性能进行评价,在噪声标准差等于30时,将本文算法与Meyer小波、偏微分方程等去噪方法进行比较,其中PSNR比Meyer小波提高2.5 d B左右,比偏微分方程算法的PSNR提高2 d B左右。仿真试验结果表明,本文算法去噪后的蝗虫切片图像去噪效果明显优于其他传统去噪算法,去噪结果在视觉效果上也优于其他传统去噪算法。 The images of locust slices with abundant textures are often negatively impacted by external noises during image acquisition,enhancement and so on. These noises then destroyed the textures of the locust slice images and hindered the study of the locust cell structures. In the frequency domain,traditional 2D wavelet transform based on tensor,in which only have two directions,horizontal and vertical,can't deal with high dimensional data effectively. Besides,the filter that constructed wavelet was isotropic,so the traditional denoising methods using wavelet denoising noise made image edge and texture blur. Shearlet often uses a compactly supported traditional wavelet function as its wavelet basis. Then this wavelet basis though translation,dilation and shear transform,makes shearlet express multiple directions.The shearlet filter is anisotropic. Shearlet uses the special frame structure,in which can preserve texture and edge so as it will not be noised. Shearlet transform was based on multi-scale geometric analysis.Shearlet can represent image sparsely. Thus this paper proposed a Shearlet algorithm based on Meyer window function. The algorithm used Meyer wavelet as the wavelet basis function,since Meyer wavelet was symmetric and infinitely differentiate. First,Meyer wavelet function and scale function were used to construct a Meyer window function. Meyer window function was used to decompose the noisy locust sliceimages in frequency domain,and compute Shearlet norms in each scale and each direction. Second,the traditional hard threshold method was used to process the Shearlet coefficient. Finally,through Shearlet inverse transform,the locust slices images were restructured. In the experimental section,two groups of experiments were set up. In the first group experiment,the proposed algorithm was compared to other denoising algorithms,such as Meyer wavelet threshold denoising algorithm,partial differential equation denoising algorithm and so on. The classical image quality evaluation index was adopted to evaluate the

关 键 词： 蝗虫切片 图像去噪 窗函数 多尺度分析 各向异性 剪切波变换

领　　域： [自动化与计算机技术] [自动化与计算机技术]