Small-kernel superresolution methods for microscanning imaging systems

Applied Optics
Jiazheng ShiJames D Howe

Abstract

Two computationally efficient methods for superresolution reconstruction and restoration of microscanning imaging systems are presented. Microscanning creates multiple low-resolution images with slightly different sample-scene phase shifts. The digital processing methods developed here combine the low-resolution images to produce an image with higher pixel resolution (i.e., superresolution) and higher fidelity. The methods implement reconstruction to increase resolution and restoration to improve fidelity in one-pass convolution with a small kernel. One method uses a small-kernel Wiener filter and the other method uses a parametric cubic convolution filter. Both methods are based on an end-to-end, continuous-discrete-continuous microscanning imaging system model. Because the filters are constrained to small spatial kernels they can be efficiently applied by convolution and are amenable to adaptive processing and to parallel processing. Experimental results with simulated imaging and with real microscanned images indicate that the small-kernel methods efficiently and effectively increase resolution and fidelity.

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Citations

Dec 21, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Russell Hardie
Aug 18, 2018·Applied Optics·Florence de la BarrièreSerge Magli
Jul 2, 2009·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Frédéric ChampagnatCaroline Kulcsár
May 22, 2010·Applied Optics·Florence de la BarrièreJoël Deschamps
Mar 14, 2012·Applied Optics·Florence de la BarrièreJean Taboury
Aug 14, 2013·Optics Express·Barry K Karch, Russell C Hardie

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