LAUSR

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. Furthermore, HASPN was applied to three out-of-distribution datasets, validating its strong generalization capability.