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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 2483169, 11 pages
https://doi.org/10.1155/2017/2483169
Research Article

Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

1College of Computer and Information, Hohai University, Nanjing, Jiangsu 211100, China
2Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
3School of Physics and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China

Correspondence should be addressed to Xin Wang

Received 7 February 2017; Revised 17 April 2017; Accepted 23 April 2017; Published 31 May 2017

Academic Editor: Xosé M. Pardo

Copyright © 2017 Xin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.