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Open AccessArticle

OCT Image Restoration Using Non-Local Deep Image Prior

College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Author to whom correspondence should be addressed.
Electronics 2020, 9(5), 784;
Received: 20 March 2020 / Revised: 28 April 2020 / Accepted: 3 May 2020 / Published: 11 May 2020
(This article belongs to the Special Issue Application of Neural Networks in Biosignal Process)
In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However, the DIP has difficulty finding a good balance between maintaining details and suppressing speckle noise. Inspired by DIP, in this paper, a sorted non-local statics which measures the signal autocorrelation in the differences between the constructed image and the input image is proposed for OCT image restoration. By adding the sorted non-local statics as a regularization loss in the DIP learning, more low-level image statistics are captured by CNN networks in the process of OCT image restoration. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual quality. View Full-Text
Keywords: OCT image restoration; deep image prior; non-local similarity; despeckle; image denoising OCT image restoration; deep image prior; non-local similarity; despeckle; image denoising
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Fan, W.; Yu, H.; Chen, T.; Ji, S. OCT Image Restoration Using Non-Local Deep Image Prior. Electronics 2020, 9, 784.

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