Blind De-Blurring of Microscopy Images for Cornea Cell Counting

TL;DR

This paper introduces two blind de-blurring methods to enhance microscopy images for cornea cell counting, reducing the number of images needed and improving patient comfort.

Contribution

It presents novel blind-deconvolution techniques for depth-from-deblur to reconstruct in-focus regions in corneal microscopy images.

Findings

Reduced number of images required for cell counting

Improved image sharpness and focus reconstruction

Enhanced patient comfort during examination

Abstract

Cornea cell count is an important diagnostic tool commonly used by practitioners to assess the health of a patient's cornea. Unfortunately, clinical specular microscopy requires the acquisition of a large number of images at different focus depths because the curved shape of the cornea makes it impossible to acquire a single all-in-focus image. This paper describes two methods and their implementations to reduce the number of images required to run a cell-counting algorithm, thus shortening the duration of the examination and increasing the patient's comfort. The basic idea is to apply de-blurring techniques on the raw images to reconstruct the out-of-focus areas and expand the sharp regions of the image. Our approach is based on blind-deconvolution reconstruction that performs a depth-from-deblur so to either model Gaussian kernel or to fit kernels from an ad hoc lookup table.