NL-CoWNet: A Deep Convolutional Encoder–Decoder Architecture for OCT Speckle Elimination Using Nonlocal and Subband Modulated DT-CWT Blocks

Abstract

Optical coherence tomography (OCT), a noninvasive diagnostic technology for identifying and treating various ocular diseases, encounters a loss of image quality due to the introduction of speckles during the image creation process, compromising the precision of disease diagnosis. Researchers have proposed numerous deep convolutional networks to address speckle artifacts in OCT images. This article presents a novel deep convolutional encoder–decoder framework called NL-CoWNet for speckle elimination in OCT images. This despeckling architecture consists of an encoder network having the topology of ResNet34, whose certain feature vectors are passed through nonlocal (NL) neural network blocks and a novel subband modulated dual-tree complex wavelet (CoW) transform (DT-CWT) blocks, followed by a decoder unit with upsampling layers and channel-wise squeeze and excitation (CSE) convolutional blocks. Our network architecture has been validated after numerous ablation studies. Qualitative and quantitative assessments with contemporary and established methodologies have proven that NL-CoWNet excels conspicuously in speckle removal while preserving the structural features of the image.