3D blind image reconstruction using combined nonlinear and statistical techniques

Blind reconstruction or deconvolution, is the process of restoring an observed image without explicit knowledge of the imaging system's point spread function (PSF). Images produced from an imaging system, for example confocal laser scanning or widefield optical microscope, are noisy and invariably blurred. For robust scientific interpretation and analysis of a typical image obtained in this way, it is essential that the image is further processed to remove these aberrations. Measurement and modelling the PSF is not ideal and the measurements are quite difficult and time consuming to perform. Here we present an algorithm to perform blind reconstruction in three dimensions using a nonlinear reconstruction algorithm called “Iterative Deconvolution algorithm” (IDA). This technique is combined with the use of statistical a priori knowledge in an iterative manner to create a robust and computationally efficient method of blind reconstruction.