Hybrid attention structure preserving network for reconstruction of under-sampled OCT images

TL;DR

This paper introduces HASPN, a novel neural network that enhances under-sampled OCT images by combining attention mechanisms and high-frequency detail reconstruction, improving image quality and speed.

Contribution

The paper proposes a hybrid attention network with a high-frequency branch for OCT image super-resolution, addressing limitations of CNNs in reconstructing fine details.

Findings

Outperforms mainstream methods in qualitative and quantitative metrics.

Demonstrates good generalization on diabetic macular edema dataset.

Effectively reconstructs retinal structures with enhanced detail.

Abstract

Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technology that provides cross-sectional images of tissues. Dense acquisition of A-scans along the fast axis is required to obtain high digital resolution images. However, the dense acquisition will increase the acquisition time, causing the discomfort of patients. In addition, the longer acquisition time may lead to motion artifacts, thereby reducing imaging quality. In this work, we proposed a hybrid attention structure preserving network (HASPN) to achieve super-resolution of under-sampled OCT images to speed up the acquisition. It utilized adaptive dilated convolution-based channel attention (ADCCA) and enhanced spatial attention (ESA) to better capture the channel and spatial information of the feature. Moreover, convolutional neural networks (CNNs) exhibit a higher sensitivity of low-frequency than high-frequency information, which may lead to a limited performance on reconstructing fine structures. To address this problem, we introduced an additional branch, i.e., textures & details branch, using high-frequency decomposition images to better super-resolve retinal structures. The superiority of our method was demonstrated by qualitative and quantitative comparisons with mainstream methods. HASPN was applied to the diabetic macular edema retinal dataset, validating its good generalization ability.