IEEE Trans Image Process - A filtering approach to edge preserving MAP estimation of images.

Tópicos

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Resumo

The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both blur and noise. The image is divided into statistically independent regions. Each region is modelled with a WSS Gaussian prior. Classical Wiener filter theory is used to generate a set of convex sets in the solution space, with the solution to the MAP estimation problem lying at the intersection of these sets. The proposed algorithm uses an underlying segmentation of the image, and a means of determining the segmentation and refining it are described. The algorithm is suitable for a range of image restoration problems, as it provides a computationally efficient means to deal with the shortcomings of Wiener filtering without sacrificing the computational simplicity of the filtering approach. The algorithm is also of interest from a theoretical viewpoint as it provides a continuum of solutions between Wiener filtering and Inverse filtering depending upon the segmentation used. We do not attempt to show here that the proposed method is the best general approach to the image reconstruction problem. However, related work referenced herein shows excellent performance in the specific problem of demosaicing.

Resumo Limpo

author present comput effici techniqu maximum posteriori map estim imag presenc blur nois imag divid statist independ region region model wss gaussian prior classic wiener filter theori use generat set convex set solut space solut map estim problem lie intersect set propos algorithm use under segment imag mean determin segment refin describ algorithm suitabl rang imag restor problem provid comput effici mean deal shortcom wiener filter without sacrif comput simplic filter approach algorithm also interest theoret viewpoint provid continuum solut wiener filter invers filter depend upon segment use attempt show propos method best general approach imag reconstruct problem howev relat work referenc herein show excel perform specif problem demosa

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