L-0-Regularized image downscaling

Publication Type

Journal Article

Publication Date

3-2018

Abstract

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.

Keywords

Image downscaling, L-0 norm sparsity, salient edges preserving

Discipline

Information Security

Research Areas

Information Systems and Management

Publication

IEEE Transactions on Image Processing

Volume

27

Issue

3

First Page

1076

Last Page

1085

ISSN

1057-7149

Identifier

10.1109/TIP.2017.2772838

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIP.2017.2772838

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