Abstract
Super-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.
Highlights
Underpinning each new piece of super-resolved knowledge are revolutionary advancements in image analysis algorithms
One advantage of such hardware-based methods is that the generated image can be analyzed by 2D processing algorithms. Another popular method is to encode the phase information in the intensity distribution by using a cylindrical lens[30] or phase mask[34,35,36] in the detection path so that 3D information is recorded in a single 2D image
In order to address the importance of accuracy, precision, and processing speed, we introduce a 3D super-resolution recovery algorithm for emitters imaged with arbitrary 3D phase masks that generate rotating point spread functions (PSFs)
Summary
Bo Shuang[1], Wenxiao Wang[2], Hao Shen[1], Lawrence J. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. One approach is to scan over different z positions and record multiple 2D images, with Ober’s group demonstrating simultaneous multiple detection planes to image 3D motion in living cells[33] One advantage of such hardware-based methods is that the generated image can be analyzed by 2D processing algorithms. In order to address the importance of accuracy, precision, and processing speed, we introduce a 3D super-resolution recovery algorithm for emitters imaged with arbitrary 3D phase masks that generate rotating PSFs. We use an alternating direction method of multipliers (ADMM)[48,49,50,51] based algorithm to deconvolute the sample positions from the 3D measurement, which records a single 2D image with encoded 3D information. As a proof-of-concept, we demonstrate that our algorithm can be used to localize single molecules within the 3D structure of a porous polystyrene film
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