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

arXiv: https://arxiv.org/abs/2406.00279 · HTML: https://mlsanity.com/paper/2406.00279/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Deep learning for undersampled MRI reconstruction](https://mlsanity.com/paper/1709.02576/) (2017)
- [BigReg: An Efficient Registration Pipeline for High-Resolution X-Ray and Light-Sheet Fluorescence Microscopy](https://mlsanity.com/paper/2404.14807/) (2024)

## Abstract (arXiv)

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.