  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.
