2020/06/05 by Alon Tchelet, Tchelet, Alon, Leonardo Mussa +3
Biochemistry, Genetics and Molecular Biology · Engineering · #Advanced Fluorescence Microscopy Techniques #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Optical Coherence Tomography Applications #Spectroscopy Techniques in Biomedical and Chemical Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.03562
openalex publication_date 2020/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.