IEEE Trans Image Process - Generative Bayesian image super resolution with natural image prior.

Tópicos

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Resumo

We propose a new single image super resolution (SR) algorithm via Bayesian modeling with a natural image prior modeled by a high-order Markov random field (MRF). SR is one of the long-standing and active topics in image processing community. It is of great use in many practical applications, such as astronomical observation, medical imaging, and the adaptation of low-resolution contents onto high-resolution displays. One category of the conventional approaches for image SR is formulating the problem with Bayesian modeling techniques and then obtaining its maximum-a-posteriori solution, which actually boils down to a regularized regression task. Although straightforward, this approach cannot exploit the full potential offered by the probabilistic modeling, as only the posterior mode is sought. On the other hand, current Bayesian SR approaches using the posterior mean estimation typically use very simple prior models for natural images to ensure the computational tractability. In this paper, we present a Bayesian image SR approach with a flexible high-order MRF model as the prior for natural images. The minimum mean square error (MMSE) criteria are used for estimating the HR image. A Markov chain Monte Carlo-based sampling algorithm is presented for obtaining the MMSE solution. The proposed method cannot only enjoy the benefits offered by the flexible prior, but also has the advantage of making use of the probabilistic modeling to perform a posterior mean estimation, thus is less sensitive to the local minima problem as the MAP solution. Experimental results indicate that the proposed method can generate competitive or better results than state-of-the-art SR algorithms.

Resumo Limpo

propos new singl imag super resolut sr algorithm via bayesian model natur imag prior model highord markov random field mrf sr one longstand activ topic imag process communiti great use mani practic applic astronom observ medic imag adapt lowresolut content onto highresolut display one categori convent approach imag sr formul problem bayesian model techniqu obtain maximumaposteriori solut actual boil regular regress task although straightforward approach exploit full potenti offer probabilist model posterior mode sought hand current bayesian sr approach use posterior mean estim typic use simpl prior model natur imag ensur comput tractabl paper present bayesian imag sr approach flexibl highord mrf model prior natur imag minimum mean squar error mmse criteria use estim hr imag markov chain mont carlobas sampl algorithm present obtain mmse solut propos method enjoy benefit offer flexibl prior also advantag make use probabilist model perform posterior mean estim thus less sensit local minima problem map solut experiment result indic propos method can generat competit better result stateoftheart sr algorithm

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