Wavelet-based filtering in scale space for data fusion
Hsi‐Chin Hsin, Ching-Chung Li · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Multisensory data often have different resolutions and measurement noises. To integrate the multiresolution information together while removing their respective noises, a fusion process is performed to improve the sensing quality. Wavelet-based multiresolution analysis provides a new approach to study the data fusion problem. In such an approach, the approximation of a signal at each scale may be interpreted as the state variables and the state transition takes place from a coarse scale to a fine scale. Through the use of Kalman theory, data fusion can be obtained by optimally estimating the finest scale representation from a set of multiscale noisy measurements, in a scale recursive form. In this paper, a generalized fusion algorithm has been developed using the dyadic-tree wavelet transform with either orthogonal or biorthogonal wavelets. We then have more choices of analyzing wavelets to be used for fusing data. Examples are given to illustrate the improved fusion performance.