Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches.

ABSTRACT:

Purpose

Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartesian grid, using a naive linear interpolation. It was shown that convolutional neural networks (CNNs) could improve pCLE image quality. Yet classical CNNs may be suboptimal in regard to irregular data.

Methods

We compare pCLE reconstruction and super-resolution (SR) methods taking irregularly sampled or reconstructed pCLE images as input. We also propose to embed a Nadaraya-Watson (NW) kernel regression into the CNN framework as a nov

SUBMITTER: Szczotka AB 

PROVIDER: S-EPMC7316691 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature