A pixel-level image fusion framework based on structural similarity

Authors:
Xiaoyan Luo;Jun Zhang;Qionghai Dai
Affiliations:
School of Electronics and Information Engineering, Beihang University, Beijing, China;School of Electronics and Information Engineering, Beihang University, Beijing, China;TNList, Department of Automation, Tsinghua University, Beijing, China
Venue:
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Year:
2009

Citing 3
Cited 0

Guest editorial: Image fusion: Advances in the state of the art

Information Fusion
Multi-focus Images Fusion Based on Data Assimilation and Genetic Algorithm

CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 06
Image quality assessment: from error visibility to structural similarity

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a novel pixel-level image fusion framework based on structural similarity (SSIM). SSIM is an image quality assessment metric developed recently through comparing local patterns of pixel intensities from luminance, contrast and structure. In our scheme, the relationship of input images is classified three kinds of cases by contrasting the SSIM value of the original images with two thresholds, a lower limit and an upper limit. Then the fusion rule can be respectively determined as maximum selection, image assimilation, and block selection according to the different relationship. In addition, a image assimilation method is revealed by ascending gradient of SSIM. The proposed scheme is implemented within some different sets of images. Simulation results show that our framework provides promising fusion performance with good perceive.