Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks
This addresses image quality issues in optical microscopy, though it appears incremental as it builds on existing deconvolution algorithms with a new PSF estimation component.
The researchers tackled the problem of spatially-variant blur in optical microscopy by developing a semi-blind deconvolution technique that combines local point spread function (PSF) estimation with a regularized Richardson-Lucy algorithm, achieving an average improvement of 1.00 dB in image SNR compared to other methods.
We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches. We deconvolved both synthetic and experimentally-acquired data, and achieved an improvement of image SNR of 1.00 dB on average, compared to other deconvolution algorithms.