Multi-mode images denoising algorithm for further improving the accuracy of oxygen saturation calculation in the retinal oximetry

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 at the edges of dual-wavelength images and further reduce the calculation error of SO2, we improve the previous 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 VST+DDID, the proposed method shows great advantage in terms of PSNR, SSIM and visual quality. The following simulation and analysis indicate that the images denoised by VST+CDDID can provide more accurate grayscale values than the images denoised by VST+DDID for retinal oximetry.