An image processing algorithm that improves the quality of raw Optical Coherence Topographic (OCT) images by reducing their blur and noise is presented. The algorithm consists of three steps: first the raw OCT images of the human eye are blended together, then the resulting image is filtered using an un-sharpening mask and finally the noise is removed using an anisotropic filter. Demonstrative images illustrating a comparison between original raw OCT images and their processed images are presented. The results depict the ability of the algorithm to reveal fine details that are not visible in the original raw image, which allow a fast-non-invasive detection of retina changes specific to eye diseases.
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