Multi image fusion based on compressive sensing
Compressive sensing provides a novel framework to acquire and to reconstruct a signal or digital image from sparse measurements acquired at sub Nyquist/Shannon sampling rate. In this paper, we present an effective image fusion scheme based on a Discrete Cosine Transform (DCT) sampling model for compressive sensing imaging. A sparse sampling model according to the DCT-based spectral energy distribution is proposed. The compressive measurements of multiple input images obtained with the proposed sampling model are fused to a composite measurement by combining their wavelet approximation coefficients and their detail coefficients separately. The combination is done by applying a weighting operation for every sampling location according to the statistical distribution. Furthermore, the fused image is reconstructed from the composite measurement by solving a problem of total variation minimization. Finally, we validate the effectiveness of the algorithm using multiple images.
Juanjuan Han Otmar Loffeld Klaus Hartmann Robert Wang
Center for Sensorsystems (ZESS), University of Siegen
国际会议
上海
英文
1463-1469
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）