Name
Title | ||
|---|---|---|
Sparse Photoacoustic Microscopy Reconstruction Based on Matrix Nuclear Norm Minimization |
Abstract | ||
|---|---|---|
As a high-resolution deep tissue imaging technology, photoacoustic microscopy (PAM) is attracting extensive attention in biomedical studies. PAM has trouble in achieving real-time imaging with the long data acquisition time caused by point-to-point sample mode. In this paper, we propose a sparse photoacoustic microscopy reconstruction method based on matrix nuclear norm minimization. We use random sparse sampling instead of traditional full sampling and regard the sparse PAM reconstruction problem as a nuclear norm minimization problem, which is efficiently solved under alternating direction method of multiplier (ADMM) framework. Results from PAM experiments indicate the proposed method could work well in fast imaging. The proposed method is also be expected to promote the achievement of PAM real-time imaging. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-73564-1_6 | international conference on machine learning |
Field | DocType | Citations |
Imaging technology,Nuclear norm minimization,Matrix completion,Pattern recognition,Matrix (mathematics),Computer science,Data acquisition,Multiplier (economics),Artificial intelligence,Sampling (statistics),Microscopy | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Fu | 1 | 104 | 33.62 |
| Naizhang Feng | 2 | 0 | 0.34 |
| Yahui Shi | 3 | 0 | 0.34 |
| Ting Liu | 4 | 0 | 0.68 |
| Mingjian Sun | 5 | 0 | 1.69 |