Work

Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation by Use of Convolutional Neural Networks

Year: 2018

Type: conference-paper

Abstract: 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 [1]. 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.

Source: Proceedings - International Conference on Image Processing

DOI: 10.1109/icip.2018.8451736

Authors: Adrian Shajkofci, Michael Liebling

Institutions: École Polytechnique Fédérale de Lausanne, Idiap Research Institute

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2790267366

FWCI: 1.245

Cites: 32

Cited by: 25

Related to: 10