24 October 2017 Denoising algorithm in dual-wavelength images of retinal oximetry using variance stabilizing transform and cross dual domain filter
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Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 1046205 (2017) https://doi.org/10.1117/12.2281401
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
Image noise can dramatically affect image processing and hemoglobin oxygen saturation (SO2) calculation accuracy in non-invasive retinal oximetry. Recently, the denoising algorithm based on Variance stabilizing transform (VST) and dual domain filter (DDID) has been proposed to address this issue by our lab. Actually, dual-wavelength retinal images belong to multi-mode images, in order to maximize the use of complementary information between the dual-wavelength images, we further improve the algorithm. Firstly, noise parameters were also estimated by mixed Poisson-Gaussian (MPG) noise model. Secondly, a novel MPG denoising algorithm which we called VST+CDDID was proposed based on VST and cross dual domain filter. To evaluate the proposed algorithm, both simulative and real experiments have been carried out and the results show that the proposed method can effectively remove MPG noise and preserve edge details. Compared with current denoising methods based on single mode, such as VST+DDID and VST+ block-matching 3D filtering (BM3D), the proposed method shows great advantage in terms of visual quality and low-contrast detail. In conclusion, VST+CDDID can effectively use the complementary information between multi-mode images and combine the advantages of both cross bilateral filter in the time domain and Short-Time Fourier Transform (STFT) in the frequency domain. And it effectively restrained ringing effect by alternating iterative.
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Yong-Li Xian, Yong-Li Xian, Yun Dai, Yun Dai, Chun-ming Gao, Chun-ming Gao, } "Denoising algorithm in dual-wavelength images of retinal oximetry using variance stabilizing transform and cross dual domain filter", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046205 (24 October 2017); doi: 10.1117/12.2281401; https://doi.org/10.1117/12.2281401
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