Date of Award
Master of Science
Electrical and Computer Engr
Aaron L Robinson
In three-dimensional fluorescence microscopy, the image formation process is inherently depth variant (DV) due to the refractive index mismatch between imaging layers, which causes depth-induced spherical aberration (SA). In this study, we present a quantitative comparison among different image restoration techniques developed based on a DV imaging model for microscopy in order to assess their ability to correct SA and their impact on restoration. The imaging models approximate DV imaging by either stratifying the object space or image space. For the reconstruction purpose, we used regularized DV algorithms with object stratification method such as the Expectation Maximization (EM), Conjugate Gradient; Principal Component Analysis based expectation maximization (PCA-EM), and Inverse filtering (IF). Reconstructions from simulated data and measured data show that better restoration results are achieved with the DV PCA-EM method than the other DV algorithms in terms of execution time and restoration quality of the image.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Rahman, Muhammad Mizanur, "Comparison of Computational Methods Developed to Address Depth-variant Imaging in Fluorescence Microscopy" (2013). Electronic Theses and Dissertations. 801.