Name
Abstract | ||
|---|---|---|
Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks. First, we divide image into several non-overlapped blocks and compute the errors between each block and its adjacent blocks. Then, the error between blocks is used to measure the structure complexity of each block, and the measurement rate of each block is adaptively determined based on the distribution of these errors. Finally, we reconstruct each block using a linear model. Experimental results show that the proposed adaptive block compressive sensing system improves the qualities of reconstructed images from both subjective and objective points of view when compared with image block compressive sensing system. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1177/1550147718781751 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Keywords | Field | DocType |
Compressive sensing, adaptive sampling, error between blocks, linear recovery | Computer science,Adaptive sampling,Linear model,Algorithm,Compressed sensing,Distributed computing | Journal |
Volume | Issue | ISSN |
14 | 6 | 1550-1477 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ran Li | 1 | 30 | 6.80 |
| Xiaomeng Duan | 2 | 1 | 1.71 |
| Yongfeng Lv | 3 | 0 | 1.01 |