Blind Deconvolution for retinal image enhancement

In this paper, a new technique is presented to enhance the blurred images obtained from retinal imaging. One of the main steps in inspecting the eye (especially the deeper image of retina) is to look into the eye using a slit-lamp apparatus that shines a monochromatic light on to the retinal surface and captures the reflection in the camera as the retinal image. While most of the cases, the image produced is quite clean and easily used by the ophthalmologists, there are many cases in which these images come out to be very blurred due to the disease in the eye such a cataract etc... in such cases, having an enhanced image can enable the doctors to start the appropriate treatment for the underlying disease. The proposed technique utilizes the Blind Deconvolution approach using Maximum Likelihood Estimation approach. Further post-processing steps have been proposed as well to extract specific regions from the image automatically to assist the doctors in visualizing these regions related to very specific diseases. The post-processing steps include Image color space conversions, thresholding, Region Growing, and Edge detection.