Open AccessReliable Single-image Denoising for Adaptive Optics Scanning Laser Ophthalmoscopy
Author(s) -
Yiwei Chen,
Yi He,
Jing Wang,
Wanyue Li,
Lina Xing,
Feng Gao,
Guohua Shi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2024/1/012024
Subject(s) - adaptive optics , artificial intelligence , computer vision , laser scanning , computer science , image quality , gaussian filter , optics , noise reduction , filter (signal processing) , laser , image (mathematics) , physics
A reliable single-image denoising method is presented for adaptive optics scanning laser ophthalmoscopy. This method firstly averaged multiple images and then used the averaged image as the reference to adjust the parameters of the filtering process that was subsequently applied to other individual images. Six filtering methods, including the mean, median, Gaussian, fast adaptive nonlocal synthetic aperture radar despeckling, K-single value decomposition, and block matching and three-dimensional filtering, were utilized. The effectiveness of our method was verified based on the comparison of sets of images without and with parameter adjustments. Furthermore, we applied the same parameter settings as those obtained from the filter adjustments of another adaptive optics scanning laser ophthalmoscope image acquired by the same instrument. The filtered images showed that the parameter-adjusted filters work well on other images, which is helpful for improving the image quality of adaptive optics scanning laser ophthalmoscope images.