BLIND DECONVOLUTION OF FLUORESCENCE MICROGRAPHS BY MAXIMUM-LIKELIHOOD-ESTIMATION

We report some recent algorithmic refinements and the resulting simula ted and real image reconstructions of fluorescence micrographs by usin g a blind-deconvolution algorithm based on maximum-likelihood estimati on. Blind-deconvolution methods encompass those that do not require ei ther calibrated or theoretical predetermination of the point-spread fu nction (PSF). Instead, a blind deconvolution reconstructs the PSF conc urrently with deblurring of the image data. Two-dimensional computer s imulations give some definitive evidence of the integrity of the recon structions of both the fluorescence concentration and the PSF. A recon structed image and a reconstructed PSF from a two-dimensional fluoresc ent data set show that the blind version of the algorithm produces ima ges that are comparable with those previously produced by a precursory nonblind version of the algorithm. They furthermore show a remarkable similarity, albeit not perfectly identical, with a PSF measurement ta ken for the same data set, provided by Agard and colleagues. A reconst ructed image of a three-dimensional confocal data set shows a substant ial axial smear removal. There is currently an existing trade-off in u sing the blind deconvolution in that it converges at a slightly slower rate than the nonblind approach. Future research, of course, will add ress this present limitation.