EdgeWaveNet: edge aware residual wavelet GAN for OCT image denoising

Sourya Sengupta; Amitojdeep Singh; Vasudevan Lakshminarayanan

doi:10.1117/12.2581110

15 February 2021 EdgeWaveNet: edge aware residual wavelet GAN for OCT image denoising

Sourya Sengupta, Amitojdeep Singh, Vasudevan Lakshminarayanan

Proceedings Volume 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications; 116010I (2021) https://doi.org/10.1117/12.2581110
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Optical coherence tomography (OCT) images suffer from speckle noise. The presence of noise may degrade the quality of the images which may further make diagnosis difficult. In this work, a wavelet transform based deep generative modeling based method has been proposed to extract multi-scale features to denoise OCT images. The OCT images contain edge information of different retinal layers, to avoid the over-smoothing effect and edge content loss, the Sobel edge detector based loss function has been designed to retain the edge information. The method is compared with other traditional and deep learning based methods in terms of commonly used image quality measures such as peak-signal-to-noise-ratio (PSNR), structural similarity (SSIM) and edge information with the variance of Laplacian.

Conference Presentation

Citation Download Citation

Sourya Sengupta, Amitojdeep Singh, and Vasudevan Lakshminarayanan "EdgeWaveNet: edge aware residual wavelet GAN for OCT image denoising", Proc. SPIE 11601, Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications, 116010I (15 February 2021); https://doi.org/10.1117/12.2581110

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