Name
Title | ||
|---|---|---|
An Optimization Approach of Compressive Sensing Recovery Using Split Quadratic Bregman Iteration with Smoothed ℓ<inf>0</inf> Norm |
Abstract | ||
|---|---|---|
An optimization algorithm for image recovery is a core issue in the field of compressive sensing (CS). This paper deeply studied the CS reconstruction algorithm based on split Bregman iteration with ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub>
norm, which enables the ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub>
norm to approximate the original ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
norm during the optimization process. Consequently, we proposed another novel algorithm improving the precision and the convergence speed based on split quadratic Bregman iteration (SQBI) with ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
norm. Besides, we analyzed its convergence by proving two monotonically decreasing theorems. Inspired by previous researches, we applied smoothed ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
norm for the optimization problem to replace the traditional ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
norm in CS. The improvement is made by using a Gaussian function to approximate the ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
norm, transforming it into a convex optimization problem, and eventually achieved a convergent solution by the steepest descent method. The experimental results show that under the same conditions, compared with other state-of-the-art algorithms, the reconstruction accuracy of the CS reconstruction algorithm based on the SQBI with smoothed ℓ
<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>
norm is improved significantly, and its convergence rate is also accelerated as well. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IPAS.2018.8708870 | 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS) |
Keywords | DocType | ISBN |
Compressive sensing,sparse representation,image recovery,split qradratic Bregman iteration,convex optimization | Conference | 978-1-7281-0248-1 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoan Yang | 1 | 24 | 7.97 |
| Yuning You | 2 | 0 | 2.70 |
| Zhengzhi Lu | 3 | 0 | 0.34 |
| Junjie Yang | 4 | 52 | 15.05 |
| Yuhao Wang | 5 | 170 | 38.41 |