Non-local Means for Stereo Image Denoising Using Structural Similarity

被引：2
|
作者：
Alkinani, Monagi H. [1 ]
El-Sakka, Mahmoud R. [1 ]
机构：
[1] Univ Western Ontario, Dept Comp Sci, London, ON N6A 5B7, Canada
来源：
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2015) | 2015年 / 9164卷
关键词：
Non-local means; Patch-based image filtering; Stereo imaging; Structural similarity index; Additive noise reduction; Disparity map; ALGORITHM;
D O I：
10.1007/978-3-319-20801-5_6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present a novel stereo image denoising algorithm. Our algorithm takes as an input a pair of noisy images of an object captured form two different directions. We use the structural similarity index as a similarity metric for identifying locations of similar patches in the input images. We adapt the Non-Local Means algorithm for denoising collected patches from the input images. We validate our algorithm on various stereo images at various noise levels. Experimental results show that the denoising performance of our algorithm is better than the original NonLocal Means and Stereo-MSE methods at low noise level (sigma <= 20).
引用
收藏
页码：51 / 59
页数：9
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