A CNN predicts depth-variant PSFs for patch-wise deconvolution of fluorescence microscopy images, with adaptive weighting to reduce artifacts, claiming 98.2% accuracy and up to 6.6 dB PSNR gain.
In: Advances in Neural Information Processing Systems
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Adaptive Weighting Depth-variant Deconvolution of Fluorescence Microscopy Images with Convolutional Neural Network
A CNN predicts depth-variant PSFs for patch-wise deconvolution of fluorescence microscopy images, with adaptive weighting to reduce artifacts, claiming 98.2% accuracy and up to 6.6 dB PSNR gain.