Paper Info
Title
Saliency-based adaptive compressive sampling of images using measurement contrast.
Abstract
Compressive Sampling (CS) achieves the sub-Nyquist image acquisition, which bringing about a rapid development of compressive imaging devices. In CS framework, the adaptive sampling scheme is an efficient approach to improving the rate-distortion performance of imaging system. However, the sampling allocation depends on the original sample image, which increases the cost and complexity of imaging system, thereby making CS lose its superiority. In this paper, we propose a saliency-based adaptive CS scheme that allocates more sampling resources to salient regions but fewer to non-salient regions. Its key idea is to extract the saliency information by using the contrast between CS measurements, thus avoiding the original sample image in the imaging system. The scheme is realized in practice without any changes of the architecture of compressive imaging device. To match our adaptive sampling scheme, we also propose a weighted global recovery model based on saliency information. This model can effectively suppress the blocking artifacts while improving the visual qualities of salient regions. Experimental results on natural images show that the proposed adaptive CS scheme improves the visual quality of reconstructed image, and has better rate-distortion performance than the existing adaptive CS schemes.
Year
DOI
Venue
2018
10.1007/s11042-017-4862-z
Multimedia Tools Appl.
Keywords
Field
DocType
Compressive sampling, Saliency detection, Measurement contrast, Adaptive sampling, Weighted global recovery
Computer vision,Pattern recognition,Computer science,Adaptive sampling,Salience (neuroscience),Artificial intelligence,Sampling (statistics),Compressed sensing,Salient
Journal
Volume
Issue
ISSN
77
10
1380-7501
Citations 
PageRank 
References 
0
0.34
15
Authors
5
Name
Order
Citations
PageRank
Ran Li1306.80
Wei He21810.20
zhenghui liu3233.78
Yan-ling Li4214.28
Zhangjie Fu5344.29