Deconvolution of wide-field fluorescence images

Specimens with very weak fluorescence or those that photobleach easily may be difficult to observe using a confocal microscope. Therefore, standard epifluorescence imaging is often a method of choice for these objects. Deconvolution is a computational image restoration technique that can remove the out-of-focus blur typical for epifluorescence images and improve both lateral and axial resolution. Deconvolution is best suited for relatively thin specimens (microorganisms, single cells, tissue sections) where it can bring significant improvement in resolution and contrast. 

MIC has the AutoQuant X software with several processing algorithms, including the iterative blind deconvolution, for wide-field fluorescence images..

The software can be used free of charge. For more information on deconvolution and for help with image acquisition for deconvolution, please contact Dr. Stanislav Vitha (Tel. 979-845-1607; vitha@tamu.edu).

Example: Blind deconvolution with AutoDeblur X.

Histone-GFP-stack-orthogonal-raw

Histone-GFP-stack-orthogonal-deconvolved

Histone-GFP fusion protein in Neurospora crassa hyphae. Specimen courtesy of Dr. Bell-Pedersen (Department of Biology, http://www.bio.tamu.edu/FACMENU/FACULTY/Bell-PedersenD.htm). A z-stack of GFP fluorescence images was acquired with z-step of 0.2 um, using Zeiss Axiophot microscope equipped with a Plan Neofluoar 100x/1.3 oil immersion objective and a Coolsnap cf camera. The raw image stack was processed with AutoDeblur X software (Media Cybernetics) using 100 iterations of blind deconvolution algorithm, taking into account spherical aberration due to refractive index mismatch.  The raw and deconvolved image stacks were then opened in ImageJ software (http://rsbweb.nih.gov/ij/) to generate orthogonal views of the image stacks. Note the large amount of out-of-focus blur in the raw image, and the improvement in contrast and resolvable detail in the deconvolved dataset. Image data was acquired by Laura Short (Department of Anthropology)  during the Spring 2009 Light Microscopy course offered by MIC (BIOL-608, Theory and Applications of Light Microscopy). Image processing by Stanislav Vitha, MIC.