We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Large-Dynamic-Range Ocular Aberration Measurement Based on Deep Learning with a Shack–Hartmann Wavefront Sensor.
- Authors
Zhang, Haobo; Zhao, Junlei; Chen, Hao; Zhang, Zitao; Yin, Chun; Wang, Shengqian
- Abstract
The Shack–Hartmann wavefront sensor (SHWFS) is widely utilized for ocular aberration measurement. However, large ocular aberrations caused by individual differences can easily make the spot move out of the range of the corresponding sub-aperture in SHWFS, rendering the traditional centroiding method ineffective. This study applied a novel convolutional neural network (CNN) model to wavefront sensing for large dynamic ocular aberration measurement. The simulation results demonstrate that, compared to the modal method, the dynamic range of our method for main low-order aberrations in ocular system is increased by 1.86 to 43.88 times in variety. Meanwhile, the proposed method also has the best measurement accuracy, and the statistical root mean square (RMS) of the residual wavefronts is 0.0082 ± 0.0185 λ (mean ± standard deviation). The proposed method generally has a higher accuracy while having a similar or even better dynamic range as compared to traditional large-dynamic schemes. On the other hand, compared with recently developed deep learning methods, the proposed method has a much larger dynamic range and better measurement accuracy.
- Subjects
WAVEFRONT sensors; DEEP learning; CONVOLUTIONAL neural networks; TONOMETERS; ROOT-mean-squares
- Publication
Sensors (14248220), 2024, Vol 24, Issue 9, p2728
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s24092728