Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer

Abstract

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.

Keywords

• retinal optical coherence tomography
• double convolution layer
• image pre-processing tasks
• optical coherence tomography systems
• degradation effects
• current deblurring research