摘要
传统的基于图像块划分的噪声强度估计方法通常先将图像划分为若干规则矩形图像块,并利用其中平滑的图像块进行噪声强度计算.然而当图像的结构信息比较丰富时,传统的方法往往会出现较大的偏差.为了解决该问题,提出一种基于自适应图像块划分的图像噪声强度估计方法.首先结合图像的空间邻域信息,将含噪图像划分为若干贴合图像局部边缘、纹理等结构信息的自适应图像块;然后根据所划分图像块的统计特性,通过散点图统计法选取出平滑的自适应图像块;最后利用平滑自适应图像块估计图像的噪声强度.实验结果表明,相比于已有的噪声估计方法,该方法在针对不同噪声强度、不同复杂程度的图像时更加准确和稳定.
An image was firstly divided into regular rectangle blocks in conventional block division based noise estimation methods, and then the noise intensity was calculated using the homogeneous blocks. However, it always leads to an over-estimation of the noise intensity when dealing with highly textured images. To solve this problem, we proposed a new noise estimation method based on an adaptive image block division algorithm. Firstly, the noisy image was decomposed into adaptive image blocks by using the spatial neighborhood pixels, where the adaptive blocks can adhere to local image structures, such as image edges and textures; then the most homogeneous adaptive blocks were detected with a scatter-plot-based statistical method; finally, the image noise intensity was calculated with the selected homogeneous adaptive blocks. Experimental results demonstrate that the proposed method is more accurate and stable than existing methods when estimating noise from images with various complexities and different noise levels.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2015年第8期1475-1482,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61273251
61401209)
中国博士后科学基金(2014T70525
2013M531364)
"十二五"民用航天技术预先研究项目(D040201)
江苏省自然科学基金青年基金(BK20140790)
江苏省普通高校研究生科研创新计划项目(CXZZ13_0211)
关键词
噪声估计
高斯噪声
自适应图像块
空间邻域信息
相似性度量
noise estimation
Gaussian noise
adaptive image block
spatial neighborhood
similarity metric
