Multi-focus image fusion based on the neighbor distance

• Published in: Pattern recognition 2013-03, Vol.46 (3), p.1002-1011
• Main Authors: Zhao, Hengjun, Shang, Zhaowei, Tang, Yuan Yan, Fang, Bin
• Format: Article
• Language: English
• Subjects: Applied sciences; Exact sciences and technology; Image analysis; Image processing; Information, signal and communications theory; Kernels; Multi-focus image fusion; Neighbor distance; Pattern recognition; Regression; Sharpness; Sharpness measure; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Standard deviation; Telecommunications and information theory; Two dimensional
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2012.09.012

The effective measurement of pixel's sharpness is a key factor in multi-focus image fusion. In this paper, a gray image is considered as a two-dimensional surface, and the neighbor distance deduced from the oriented distance in differential geometry is used as a measure of pixel's sharpness, where the smooth image surface is restored by kernel regression. Based on the deduced neighbor distance filter, we construct a multi-scale image analysis framework, and propose a multi-focus image fusion method based on the neighbor distance. The experiments demonstrate that the proposed method is superior to the conventional image fusion methods in terms of some objective evaluation indexes, such as spatial frequency, standard deviation, average gradient, etc. ► We propose a multi-focus image fusion based on the neighbor distance. ► The neighbor distance can effectively measure the sharpness of image pixels with different focus settings. ► The neighbor distance is deduced from oriented distance in differential geometry. ► The smooth image surface is fitted by kernel regression.