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Title: A Novel Deep Learning Approach for the Removal of Speckle Noise from Optical Coherence Tomography Images Using Gated Convolution�Deconvolution Structure
Authors: Menon, S.N.
Vineeth, Reddy, V.B.
Yeshwanth, A.
Anoop, B.N.
Rajan, J.
Issue Date: 2020
Citation: Advances in Intelligent Systems and Computing, 2020, Vol.1024, , pp.115-126
Abstract: Optical coherence�tomography (OCT) is an imaging�technique widely used to image retina. Speckle noise in OCT images generally degrades�the quality of the OCT images�and makes the clinical diagnosis tedious. This paper proposes a new deep neural network despeckling scheme called gated convolution�deconvolution structure (GCDS). The robustness of the proposed method is evaluated on the publicly available OPTIMA challenge dataset and Duke dataset. The quantitative analysis based on PSNR shows that the results of the proposed method are superior to other state-of-the-art methods. The application of the proposed method for segmenting retinal cyst from OPTIMA challenge dataset was also studied. � 2020, Springer Nature Singapore Pte Ltd.
Appears in Collections:2. Conference Papers

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