Name
Title | ||
|---|---|---|
Speckle Reduction In Optical Coherence Tomography Via Super-Resolution Reconstruction |
Abstract | ||
|---|---|---|
Reducing speckle noise from the optical coherence tomograms (OCT) of human retina is a fundamental step to a better visualization and analysis in retinal imaging, as thus to support examination, diagnosis and treatment of many eye diseases. In this study, we propose a new method for speckle reduction in OCT images using the super-resolution technology. It merges multiple images for the same scene but with sub-pixel movements and restores the missing signals in one pixel, which significantly improves the image quality. The proposed method is evaluated on a dataset of 20 OCT volumes (5120 images), through the mean square error, peak signal to noise ratio and the mean structure similarity index using high quality line-scan images as reference. The experimental results show that the proposed method outperforms existing state-of-the-art approaches in applicability, effectiveness, and accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8856445 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Iterative reconstruction,Computer vision,Peak signal-to-noise ratio,Optical coherence tomography,Speckle pattern,Computer science,Image quality,Coherence (physics),Image segmentation,Artificial intelligence,Speckle noise | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Zhao | 1 | 0 | 0.34 |
| Yitian Zhao | 2 | 246 | 33.15 |
| Zhili Chen | 3 | 35 | 4.16 |
| Y. Zhao | 4 | 277 | 33.44 |
| Jianlong Yang | 5 | 18 | 4.01 |
| Yan Hu | 6 | 0 | 0.34 |
| Jun Cheng | 7 | 214 | 20.65 |
| Jiang Liu | 8 | 299 | 42.50 |