摘要
运用方向微分鉴别运动模糊方向的基本思想是将原图像视为各向同性的一阶马尔可夫随机过程。但实际处理效果并不理想,其主要原因是很多图像并不严格满足各向同性的一阶马尔可夫随机过程。从整个图像来看,物体形状、平坦区域等因素都会弱化这一物理前提,造成微分图像计算不准确。为此提出一种优化方法,先提取多个局部方差较大的特征块,再分块进行运动模糊方向的鉴别,统计分块结果。实验结果表明,本文方法在传统的基于方向微分的加权平均法鉴别误差较大时,依然可以取得不错的鉴别精度。
The basic idea of identifying the direction of motion blurring by directional derivatives is that the original image is an isotropic first-order Markov random process. However, the real result are not good. The main reason is that the images do not meet this assumption strictly. Judging from the entire image, the shape of the objects, flat areas, and some other factors tend to weaken the physical premise, and cause inaccurate calculation result of the differential image. In this paper, we propose an optimization method. First, multiple feature blocks are extracted via the image local variance, and then the statistical results of the motion blur direction identification from every block are retrieved. The experimental results show that our method can still obtain good identification accuracy even when the error of the identification of traditional directional derivative method is big.
出处
《中国图象图形学报》
CSCD
北大核心
2013年第7期776-782,共7页
Journal of Image and Graphics
基金
国家自然科学基金项目(60672072
60832003)
可视化计算与虚拟现实四川省重点实验室项目(PJ2012005)
宁波市自然科学基金项目(2012A610047)
关键词
方向微分
马尔可夫随机过程
局部方差
加权平均法
分块统计
directional derivative
Markov random process
local variance
weighted average method
blocked statistics method
