Name
Abstract | ||
|---|---|---|
In recent years, block compressed sampling (BCS) has emerged as a considerable attractive sampling technology for image acquisition. However, the general BCS approaches ignore the information distribution in the same image sub-block, and may lead to unfair allocation of sampling resources. In this paper, we propose a novel compressed sampling scheme by employing the idea of adaptive partition. In the proposed scheme, images are adaptively partitioned based on their saliency information through clustering, and pixels with similar saliency are gathered in the same sub-blocks. Sampling rates for those blocks, in turn, are computed on the basis of their saliency values, respectively. Therefore the sampling resources are allocated with fairer and more equitable sharing by all sub-blocks. Experimental results show that the proposed scheme has better visual effect and obtains higher image reconstruction accuracy than existing ones. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-017-5249-x | Multimedia Tools Appl. |
Keywords | Field | DocType |
Compressed sampling, Image, Saliency, Clustering | Iterative reconstruction,Computer vision,Pattern recognition,Salience (neuroscience),Computer science,Artificial intelligence,Sampling (statistics),Pixel,Cluster analysis,Partition (number theory),Sampling scheme | Journal |
Volume | Issue | ISSN |
78 | 1 | 1573-7721 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Si-wang Zhou | 1 | 82 | 10.27 |
| Zhineng Chen | 2 | 192 | 25.29 |
| Qian Zhong | 3 | 43 | 12.91 |
| Heng Li | 4 | 21 | 7.48 |