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

Xin Wang, Chunyan Zhang, Chen Ning, Yuzhen Zhang and Guofang Lv

Mathematical Problems in Engineering, 2017, vol. 2017, 1-11

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2483169

DOI: 10.1155/2017/2483169

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