Multiframe information fusion for gray-scale and spatial enhancement of images

An imaging system produces a degraded measurement of a real-valued quantity that varies temporally, spectrally, and spatially. When there are multiple measurements of the same scene, it is possible to combine the nonredundant information in those measurements and produce an improved image that has more information than any of the measurements does alone. This type of reconstruction is known as image fusion. Depending on the diversity of information in the measurements, it is possible to achieve temporal, spectral, spatial, and gray-scale improvements with an image fusion algorithm. In this paper, we are proposing an image fusion algorithm that produces an image of higher spatial and gray-scale information from multiple measurements. The algorithm estimates the spatial and illumination correlation between multiple measurements, and employs a set-theoretic reconstruction technique.