Compressive Confocal Microscopy: 3D Reconstruction Algorithms

In this paper, a new approach for Confocal Microscopy (CM) based on the framework of compressive sensing is developed. In the proposed approach, a point illumination and a random set of pinholes are used to eliminate out-of-focus information at the detector. Furthermore, a Digital Micromirror Device (DMD) is used to efficiently scan the 2D or 3D specimen but, unlike the conventional CM that uses CCD detectors, the measured data in the proposed compressive confocal microscopy (CCM) emerge from random sets of pinhole illuminated pixels in the specimen that are linearly combined (projected) and measured by a single photon detector. Compared to conventional CM or programmable array microscopy (PAM), the number of measurements needed for nearly perfect reconstruction in CCM is significantly reduced. Our experimental results are based on a testbed that uses a Texas Instruments DMD (an array of 1024x768; 13.68x13.68 mu m(2) mirrors) for computing the linear projections of illuminated pixels and a single photon detector is used to obtain the compressive sensing measurement. The position of each element in the DMD is defined by the compressed sensing measurement matrices. Three-dimensional image reconstruction algorithms are developed that exploit the inter-slice spatial image correlation as well as the correlation between different 2D slices. A comprehensive performance comparison between several binary projection patterns is shown. Experimental and simulation results are provided to illustrate the features of the proposed systems.