However, the aforementioned measurement method may lead to dramatic misinterpretations of the measured data.6 They are caused by many factors such as image noise, the imaging errors of the detection system, vessel reflex, fundus pigmentation extinction, and so on. In particular, noise can affect the subsequent feature extraction and calculation accuracy of . Thamm et al.6 proposed a data processing method for the improvement of the primary information leading to a reduction of the uncertainties of the derived . The approach may be the most accurate one because they took vessel reflex, imaging errors of the detection system, and the noise of the signal into account and developed a sophisticated model to simultaneously measure over a broad wavelength range using a spectrometer. But the major shortcoming is the limitation on measurements at a small region on the fundus including a retinal vessel and its surroundings. They could not give a complete, two-dimensional (2D) mapping of the in the retinal vascular tree, which is needed for clinical diagnostics. Hammer and Schweitzer7 used polarized light to enhance the reflection signal and improve the accuracy of oximetry. Actually, differences in choroidal pigmentation influence the measurement of and should be taken into account. Beach et al.8 have modified the calculation formula for to reduce the influence of choroidal pigmentation and have significantly reduced variation in the arterial measurements among subjects. In order to increase the measurement accuracy of , Smith9 developed a series of oximetric equations that explicitly consider the effects of multiple light paths, including the effects of backscattering by red blood cells and lateral diffusion of light in the ocular fundus. Hammer et al.10 also took the influence of vessel diameter as well as fundus pigmentation on the into account and compensated for that by introducing linear compensation terms into the oximetry equation. Recently, Hardarson and Jonsson11 studied whether and how image quality affects measurements of in retinal vessels. They employed a newly developed software tool to automatically grade the images on a scale of 0 to 1 according to the quality of the images. The quality grade was composed of the assessment of focus and contrast. They concluded that the poor image quality could lead to lower measured venous and could also affect measurements of arteries in more extreme cases. But they did not fully analyze the reasons for the decrease of image quality, nor did they address the noise problem of spectrophotometric images. It is necessary and important to study a denoising algorithm for the dual-wavelength images, since the image noise can affect the subsequent image processing and lead to substantial uncertainties in the calculated .