IEEE Trans Image Process - Image interpolation via regularized local linear regression.

Tópicos

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Resumo

The linear regression model is a very attractive tool to design effective image interpolation schemes. Some regression-based image interpolation algorithms have been proposed in the literature, in which the objective functions are optimized by ordinary least squares (OLS). However, it is shown that interpolation with OLS may have some undesirable properties from a robustness point of view: even small amounts of outliers can dramatically affect the estimates. To address these issues, in this paper we propose a novel image interpolation algorithm based on regularized local linear regression (RLLR). Starting with the linear regression model where we replace the OLS error norm with the moving least squares (MLS) error norm leads to a robust estimator of local image structure. To keep the solution stable and avoid overfitting, we incorporate the l(2)-norm as the estimator complexity penalty. Moreover, motivated by recent progress on manifold-based semi-supervised learning, we explicitly consider the intrinsic manifold structure by making use of both measured and unmeasured data points. Specifically, our framework incorporates the geometric structure of the marginal probability distribution induced by unmeasured samples as an additional local smoothness preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance with the state-of-the-art interpolation algorithms, especially in image edge structure preservation.

Resumo Limpo

linear regress model attract tool design effect imag interpol scheme regressionbas imag interpol algorithm propos literatur object function optim ordinari least squar ol howev shown interpol ol may undesir properti robust point view even small amount outlier can dramat affect estim address issu paper propos novel imag interpol algorithm base regular local linear regress rllr start linear regress model replac ol error norm move least squar mls error norm lead robust estim local imag structur keep solut stabl avoid overfit incorpor lnorm estim complex penalti moreov motiv recent progress manifoldbas semisupervis learn explicit consid intrins manifold structur make use measur unmeasur data point specif framework incorpor geometr structur margin probabl distribut induc unmeasur sampl addit local smooth preserv constraint optim model paramet can obtain closedform solut solv convex optim problem experiment result benchmark test imag demonstr propos method achiev competit perform stateoftheart interpol algorithm especi imag edg structur preserv

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