基于噪声类型及强度估计的狭叶锦鸡儿叶切片图像盲去噪  

Blind image denoising of microscopic slices image of Caragana stenophylla Pojark based on noise type and intensity estimation

作　　者：王海超[1] 王春光[1] 宗哲英[1] 殷晓飞[1] 张文霞[1] 王晓蓉[1] 张海军[1] 李靖[1] 刘涛[1] 

机构地区：[1]内蒙古农业大学机电工程学院,呼和浩特010018

出　　处：《农业工程学报》2017年第10期229-238,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：内蒙古自治区高等学校科学研究项目(NJZY070);内蒙古自治区博士研究生科研创新项目(B20151012902Z);内蒙古自治区自然科学基金项目(2017MS0514);内蒙古自治区自然科学基金项目(2017MS0361)

摘　　要：狭叶锦鸡儿叶切片显微图像在获取过程中不可避免的受到噪声污染,会对后续处理造成不良影响。针对现有噪声类型未知,去噪算法存在速度慢、效果不理想等问题,该文提出图像噪声类型估计-强度估计-去噪这一处理过程,实现对狭叶锦鸡儿叶切片显微图像降噪目的。首先采用平滑区直方图重构和拟合法确定噪声类型;然后在此基础上,应用基于图像块的SVD(singular valuable decomposition,SVD)域图像噪声强度估计法对噪声标准差进行估计;最后在确定噪声类型和强度基础上,采用几何均值滤波(geometric mean filtering,GMF)和三维块匹配滤波(block-matching and 3-D filtering,BM3D)对图像进行联合去噪。试验结果表明:该文噪声类型估计法估计出切片图像噪声类型为加性高斯噪声,高斯函数对随机选取的15幅狭叶锦鸡儿叶切片图像平滑区域直方图数据点拟合优度2R均值为0.996,平均均方根误差RMSE(root mean squared error,RMSE)为0.144 6;采用该文噪声强度估计法估计出的切片图像噪声标准差???[2.5,4.0],处理标准差较小噪声,该文算法处理精度、运行速度和稳定性等方面存在明显优势;GMF-BM3D算法在较好去除图像噪声同时,极大的保留了图像纹理、边缘和细节等信息,同时极大的提高了算法运行速度,处理后的图像BRISQUE(blind/referenceless image spatial quality evaluator,BRISQUE)值为10左右,相当于原图BRISQUE值的1/2左右。与传统BM3D算法相比,去噪效果相当,但耗时约相当于传统BM3D算法的1/9。与小波去噪算法(wavelet threshold,WT)算法相比,虽速度相对较慢,但去噪后图像BRISQUE值比使用WT法低4左右。因此,该算法较好实现了对狭叶锦鸡儿叶切片图像准确降噪,为其后续处理提供了可靠技术支持。The microscopic images from leaf slices of Caragana stenophylla Pojark are inevitably corrupted by noise in obtaining, which will have a negative effect on its subsequent processes. The unpitched sound is usually treated as an additive Gaussian sound in order to denoise the natural images. It has not yet been known whether the unpitched sound and the additive Gaussian sound display the same distribution. The choice of the unpitched sound model will determine the estimation on its noise level and its subsequent choice of the denoising algorithm. BM3D （block-matching and three-dimensional filtering） is universally regarded as the best denoising algorithm, since it keeps the maximum information about texture, border, and other details of the image while denoising effectively. But the speed of BM3D is limited owing to too much calculation and its practical use is also limited because it needs the noise variation as a pretested input. SVD （singular valuable decomposition） is one of the best denoising algorithms but the noise of the image and its information can not be completely separated. The type of image, the level of the noise, and the change of color all affect the validity of the algorithm. This paper proposed a process, involving the estimation on the image type, the noise level and the algorithm of denoising, to solve the problems mentioned above and to denoise the microscopic images from leaf slices of Caragana stenophylla Pojark. The noise type was recognized as Gaussian noise by the rebuilding and fitting of the smooth histogram domain. The values, abstracted from the smooth histogram domain of 15 randomly selected microscopic images from leaf slices of Caragana stenophylla Pojark, were fitted with the Gaussian function, with the R2 value of 0.996 and the RMSE （root mean squared error） value of 0.144 6. And then the standard deviation of the noise was estimated via the estimation on noise level with the help of SVD domain image block. In the algorithm estimation, an initial estimative value was

关 键 词：图像处理 高斯噪声 评估 切片图像 狭叶锦鸡儿 噪声强度 图像去噪 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]