Open AccessDeblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer
Author(s) -
Lian Jian,
Hou Sujuan,
Sui Xiaodan,
Xu Fangzhou,
Zheng Yuanjie
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.0016
Subject(s) - deblurring , computer science , optical coherence tomography , convolutional neural network , deconvolution , convolution (computer science) , artificial intelligence , deep learning , pattern recognition (psychology) , artificial neural network , image (mathematics) , image restoration , computer vision , algorithm , image processing , optics , physics
Various image pre‐processing tasks in optical coherence tomography (OCT) systems involve reversing degradation effects (e.g. deblurring). Current deblurring research mainly focuses on how to build suitable degradation models using deconvolution operators. However, model‐based solutions may not work well in many scenarios. To solve this problem, the authors propose a non‐model architecture, called a deep convolutional neural network, to address parameter‐free situations. The proposed solution employs a deep learning strategy to bridge the gap between traditional model‐based methods and neural network architectures. Experiments on retinal OCT images demonstrate that the proposed approach achieves superior performance compared with the state‐of‐the‐art model‐based OCT deblurring methods.