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15 March 2023 Image fusion in correlation based superresolution imaging using convolutional neural networks (Conference Presentation)
Lior M. Beck, Ariel Halfon, Uri Rossman, Assaf Shocher, Michal Irani, Dan Oron
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Proceedings Volume PC12386, Single Molecule Spectroscopy and Superresolution Imaging XVI; PC123860F (2023) https://doi.org/10.1117/12.2648282
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
A temporal correlation superresolution image is based on the variance of the recorded photon time trace, while its resolution is higher than that of the complementary intensity image, it is noisier. Both images, the intensity and correlation based, are fed into a deep convolutional neural network (CNN), which produces an image that is optimized to have higher resolution than the intensity image and less noise than the correlation image. The image then passes through separate linear networks that mimic the physical blurring of the imaging setup. Preliminary experimental results show similar resolution to the experimental superresolution image with less noise.
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Lior M. Beck, Ariel Halfon, Uri Rossman, Assaf Shocher, Michal Irani, and Dan Oron "Image fusion in correlation based superresolution imaging using convolutional neural networks (Conference Presentation)", Proc. SPIE PC12386, Single Molecule Spectroscopy and Superresolution Imaging XVI, PC123860F (15 March 2023); https://doi.org/10.1117/12.2648282
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KEYWORDS
Image fusion
Super resolution
Convolutional neural networks
Image resolution
Photons
Optical imaging
3D image processing
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