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EdgeWaveNet: edge aware residual wavelet GAN for OCT image denoising by Sourya Sengupta; Amitojdeep Singh; Vasudevan Lakshminarayanan is a scholarly article available to read on EtoBox.

What is EdgeWaveNet: edge aware residual wavelet GAN for OCT image denoising about?

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.

Author

Sourya Sengupta; Amitojdeep Singh; Vasudevan Lakshminarayanan

Publisher

SPIE

Published

2021

Language

NO