Efficient image super-resolution integration

Publication Type

Journal Article

Publication Date

6-2018

Abstract

The super-resolution (SR) problem is challenging due to the diversity of image types with little shared properties as well as the speed required by online applications, e.g., target identification. In this paper, we explore the merits and demerits of recent deep learning-based and conventional patch-based SR methods and show that they can be integrated in a complementary manner, while balancing the reconstruction quality and time cost. Motivated by this, we further propose an integration framework to take the results from FSRCNN and A+ methods as inputs and directly learn a pixel-wise mapping between the inputs and the reconstructed results using the Gaussian conditional random fields. The learned pixel-wise integration mapping is flexible to accommodate different upscaling factors. Experimental results show that the proposed framework can achieve superior SR performance compared with the state of the arts while being efficient.

Keywords

Image super-resolution, Image processing, Gaussian conditional random fields

Discipline

Information Security

Research Areas

Information Systems and Management

Publication

Visual Computer

Volume

34

Issue

6-8

First Page

1065

Last Page

1076

ISSN

0178-2789

Identifier

10.1007/s00371-018-1554-2

Publisher

Springer

Additional URL

https://doi.org/10.1007/s00371-018-1554-2

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