Abstract
We introduce functionals on image spaces that can be readily interpreted as image priors, i.e., probability distributions expressing one’s uncertainty before having observed any (image) data. However, as opposed to previous work in this area, not the actual images are considered but their observed version, i.e., we assume the images are obtained be means of a linear aperture, which is typically taken to be Gaussian. More specifically, we consider those functionals that are invariant under blurring of the observed images and the main aim is to fully describe the class of admissible functionals under these assumptions. As it turns out, this class of ‘priors’ is rather large and adding additional constraints may be considered to restrict the possible solutions.
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Loog, M., Lauze, F. (2007). Blur Invariant Image Priors. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_57
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DOI: https://doi.org/10.1007/978-3-540-72823-8_57
Publisher Name: Springer, Berlin, Heidelberg
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