L -Regularized Image Downscaling

In this paper, we propose a novel L 0 -regularized optimization framework for image downscaling. The optimization is driven by two L 0 -regularized priors. The first prior, gradient-ratio prior, is based on the observation that the number of edges in the downscaled image is approximately inverse squ...

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Bibliographic Details
Published inIEEE transactions on image processing Vol. 27; no. 3; pp. 1076 - 1085
Main Authors Junjie Liu, Shengfeng He, Lau, Rynson W. H.
Format Journal Article
LanguageEnglish
Published IEEE 01.03.2018
Subjects
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L ₀ norm sparsity
Computer science
Image downscaling
Image edge detection
Image resolution
Kernel
Linear programming
Optimization
salient edges preserving
Visual perception
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Summary:In this paper, we propose a novel L 0 -regularized optimization framework for image downscaling. The optimization is driven by two L 0 -regularized priors. The first prior, gradient-ratio prior, is based on the observation that the number of edges in the downscaled image is approximately inverse square proportional to the downscaling factor. By introducing L 0 norm sparsity to the gradient ratio, the downscaled image is able to preserve the most salient edges as well as the visual perception of the original image. The second prior, downsampling prior, is to constrain the downsampling matrix so that pixels of the downscaled image are estimated according to those optimal neighboring pixels. Extensive experiments on the Urban100 and BSDS500 data sets show that the proposed algorithm achieves superior performance over the state-of-the-arts, in terms of both quality and robustness.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2772838