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A Global-Local Approach to Extracting Deformable Fashion Items from Web Images Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Enhanced Joint Trilateral Up-sampling for Super-Resolution

Authors : Liang Yuan, Xin Jin, Chun Yuan

Published in: Advances in Multimedia Information Processing - PCM 2016

Publisher: Springer International Publishing

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Abstract

In this work, we propose a new depth image super-resolution method. We use low resolution depth image, refined high resolution color image and generated HR depth image to conduct iterative joint trilateral up-sampling. During the process of up-sampling, we put forward an algorithm to smooth the area in color image with overmuch texture to solve the texture copying problem. Based on the assumption that LR image is a counterpart of HR image with missing pixels, we defined an evaluation criterion to ensure the convergence of iteration and simultaneously make the final generated image close to the true HR depth image as far as possible. Our approach can generate HR depth image with sharp edges, none texture copying and little noises. Experiments are conducted on various datasets including Middlebury to demonstrate the superiority of the proposed method and show the improvement over state-of-the-art methods.

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Metadata
Title
Enhanced Joint Trilateral Up-sampling for Super-Resolution
Authors
Liang Yuan
Xin Jin
Chun Yuan
Copyright Year
2016
Book
Advances in Multimedia Information Processing - PCM 2016

Print ISBN: 978-3-319-48895-0

Electronic ISBN: 978-3-319-48896-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48896-7_51