Name
Title | ||
|---|---|---|
Recursive intelligent matching pursuit method for image sequences reconstruction based on l<inf>0</inf> minimization |
Abstract | ||
|---|---|---|
This paper addresses the image sequences reconstruction by l
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
minimization, which is an NP-hard problem with high computational complexity. To solve it, we propose a novel recursive intelligent matching pursuit(RIMP) algorithm in this paper. The idea of RIMP lies in utilizing the recursive reconstruction to reduce the sparsity and applying the superiorities of intelligent optimization algorithm to solve the l
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
minimization essentially, which is beneficial to improving the reconstruction accuracy. To reduce the computational complexity, some matching strategies of greedy algorithm are used to design the intelligent searching strategies to accelerate the reconstruction speed. Also, based on the high reconstruction accuracy of the intelligent searching, RIMP can significantly improve the reconstruction accumulation in recursive reconstruction for image sequences. Numerical simulations on several image sequences have been used to demonstrate that the theoretical reconstruction performance of RIMP can be achieved. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/I2MTC.2017.7969702 | 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) |
Keywords | DocType | ISBN |
recursive intelligent matching pursuit method,image sequences reconstruction,l0 minimization,NP-hard problem,computational complexity,recursive intelligent matching pursuit algorithm,RIMP algorithm,recursive reconstruction,intelligent optimization algorithm,greedy algorithm,intelligent searching strategies,numerical simulations | Conference | 978-1-5090-3597-7 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| dan li | 1 | 13 | 2.64 |
| Chaoran Liu | 2 | 0 | 0.34 |
| Xuan Liu | 3 | 170 | 18.61 |
| Zhaojun Wu | 4 | 17 | 4.27 |
| Qiang Wang | 5 | 601 | 84.65 |
| Yi Shen | 6 | 163 | 25.21 |