Multi-focus image fusion based on sparse decomposition and background detection
Multi-focus image fusion based on sparse decomposition and background detection
复制标题
基于稀疏分解和背景检测的多焦点图像融合
DOI：
10.1016/j.dsp.2016.07.010
复制
发表时间：
2016-11-01
影响因子：
2.9
通讯作者：
Jiao Doudou
中科院分区：
文献类型：
--
作者：
Zhang Baohua;Lu Xiaoqi;Jiao Doudou
图像融合的目标是准确、全面地描述新场景中多源图像的互补信息。传统的融合方法容易产生伪影和边缘模糊等副作用。针对这些问题，提出了一种基于鲁棒主元分析（RPCA）和引导滤波的融合算法。制导滤波器能有效地保持图像的边缘，常用于增强图像而不失真细节。考虑到引导滤波器对边缘和平坦区域的处理不同，本文利用引导滤波器对源图像的稀疏分量进行滤波，生成边缘保留、背景增强的增强图像。然后利用增强后的图像与对应源图像之间的差分图像的空间频率图来检测源图像的聚焦区域。最后，利用形态学算法得到精确的融合决策图。实验结果表明，该方法明显提高了融合性能，优于现有的融合方法。(C)2016 Elsevier Inc. All rights reserved.
The goal of image fusion is to accurately and comprehensively describe complementary information of multiple source images in a new scene. Traditional fusion methods are easy to produce side-effects which cause artifacts and blurred edges. To solve these problems, a novel fusion algorithm based on robust principal component analysis (RPCA) and guided filter is proposed. The guided filter can preserve the edges effectively, which is often used to enhance the images without distort the details. Considering edges and flat area are treated differently by the guided filter, in this paper, sparse component of the source image is filtered by the guided filter to generate the enhanced image which contains the preserved edges and the enhanced background. And then the focused regions of the source images are detected by spatial frequency map of the difference images between the enhanced image and the corresponding source image. Finally, morphological algorithm is used to obtain precise fusion decision map. Experimental results show that the proposed method improves the fusion performance obviously which outperforms the current fusion methods. (C) 2016 Elsevier Inc. All rights reserved.