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Adaptive image resizing based on continuous-domain stochastic modeling.
IEEE Trans Image Process ; 23(1): 413-23, 2014 Jan.
Article em En | MEDLINE | ID: mdl-24235249
We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and stochastic auto-regressive models, and exploit it for optimally choosing Sobolev parameters from available pixel values. To this aim, we use exact continuous-to-discrete mapping of the auto-regressive model that is based on symmetric exponential splines. The mapping is computationally efficient, and we exploit it for maximizing an approximated Gaussian likelihood function.We account for non-Gaussian Lévy-type processes by deriving a more robust estimator that is based on the sample auto-correlation sequence. Both estimators use multiple initialization values for overcoming the local minima structure of the fitting criteria. Experimental image resizing results indicate that the auto-correlation criterion can cope better with non-Gaussian processes and model mismatch. Our work demonstrates the importance of the auto-correlation function in adaptive image interpolation and image modeling tasks, and we believe it is instrumental in other image processing tasks as well.
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Base de dados: MEDLINE Assunto principal: Algoritmos / Interpretação de Imagem Assistida por Computador / Aumento da Imagem / Modelos Estatísticos Idioma: En Ano de publicação: 2014 Tipo de documento: Article
Buscar no Google
Base de dados: MEDLINE Assunto principal: Algoritmos / Interpretação de Imagem Assistida por Computador / Aumento da Imagem / Modelos Estatísticos Idioma: En Ano de publicação: 2014 Tipo de documento: Article