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Three Dimensional Image Inpainting

Authors: Satoru Morita 0001;

Three Dimensional Image Inpainting

Abstract

Recently the method restoring an old picture using the local differential equation on the basis of the geometric measure is proposed. It is necessary to restore an old film and a medical image with noise as well as an old picture. So we extend the method applying for the two dimentional image such as a picture to the three dimensional image such as a time sequence image and a medical image. It is necessary to obtain an object boundary from the original image in order to generate a composite image of good quality, which is difficult to distinguish from the original image. If an object boundary can not be detected, it is difficult to remove the object. In this study, we propose a method for detecting an object boundary and removing it and inpainting its image in a manner that makes it difficult to distinguish from the original image. We extend the image partition method based on the level set method to the method applying for the movie and the medical image to detect an object boundary. We demonstrate its effectiveness by removing a terop from a movie and a tumor from a three dimensional medical image.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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