What we believe images are determines how we take actions in image and low-level vision analysis. In the Bayesian framework, it is known as the importance of a good image prior model. This paper intends to give a concise overview on the vision foundation, mathematical theory, computational algorithms, and various classical as well as unexpected new applications of the BV (bounded variation) image model, first introduced into image processing by Rudin, Osher, and Fatemi in 1992 [Physica D, 60:259-268].
Institute for Mathematics and Its Applications>IMA Preprints Series
Chan, Tony F.; Shen, Jianhong.
A good image model eases restoration - on the contribution of Rudin-Osher-Fatmi's BV image model.
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