Abstract
This paper describes a Bayesian method for constructing a super-resolved surface model by combining information from a set of images of the given surface. We develop the theory and algorithms in detail for the 2-D reconstruction problem, appropriate for the case where all images are taken from roughly the same direction and under similar lighting conditions. We show the results of this 2-D reconstruction on Viking Martian data. These results show dramatic improvements in both spatial and gray-scale resolution. The Bayesian approach uses a neighbor correlation model as well as pixel data from the image set. Some extensions of this method are discussed, including 3-D surface reconstruction and the resolution of diffraction blurred images.
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© 1996 Springer Science+Business Media Dordrecht
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Cheeseman, P., Kanefsky, B., Kraft, R., Stutz, J., Hanson, R. (1996). Super-Resolved Surface Reconstruction from Multiple Images. In: Heidbreder, G.R. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8729-7_23
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DOI: https://doi.org/10.1007/978-94-015-8729-7_23
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4407-5
Online ISBN: 978-94-015-8729-7
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