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In this paper, we investigate limiting behavior of linear dynamic systems driven by random stochastic matrices. We introduce and study the new concepts of partial ergodicity and ℓ1-approximation of a given chain of stochastic matrices. We show that partial ergodicity of a chain is invariant under ℓ1-approximations. We also introduce an infinite flow graph of a random chain and use the connectivity...
This paper is devoted to the combination of image priors in Super Resolution (SR) image reconstruction. Taking into account that each combination of a given observation model and a prior model produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational posterior distribution approximation on each posterior will produce as many posterior approximations...
In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. A sparse image prior based on the horizontal and vertical first order differences is combined with a non-sparse SAR prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution...
Dubois and Prade (1990) introduced the lower and upper approximations of fuzzy sets in a Pawlak approximation space, which initiated the concept of rough fuzzy sets. The present paper aims to combine the theories of fuzzy sets, rough sets and soft sets together. We consider the lower and upper approximations of fuzzy sets in terms of soft sets, which give rise to the new concepts of soft-rough approximations...
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