IEEE Trans Image Process - Single image super-resolution with non-local means and steering kernel regression.

Tópicos

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Resumo

Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.

Resumo Limpo

imag superresolut sr reconstruct essenti illpos problem import design effect prior purpos propos novel imag sr method learn nonloc local regular prior given lowresolut imag nonloc prior take advantag redund similar patch natur imag local prior assum target pixel can estim weight averag neighbor base consider util nonloc mean filter learn nonloc prior steer kernel regress learn local prior assembl two complementari regular term propos maximum posteriori probabl framework sr recoveri thorough experiment result suggest propos sr method can reconstruct higher qualiti result quantit perceptu

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