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Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolutionis important for the quantification and analysis of retinal vasculature.However, the resolution of OCTA images is inversely proportional to the fieldof view at the same sampling frequency, which is not conducive to cliniciansfor analyzing larger vascular areas. In this paper, we propose a novelSparse-based domain Adaptation Super-Resolution network (SASR) for thereconstruction of realistic 6x6 mm2/low-resolution (LR) OCTA images tohigh-resolution (HR) representations. To be more specific, we first perform asimple degradation of the 3x3 mm2/high-resolution (HR) image to obtain thesynthetic LR image. An efficient registration method is then employed toregister the synthetic LR with its corresponding 3x3 mm2 image region withinthe 6x6 mm2 image to obtain the cropped realistic LR image. We then propose amulti-level super-resolution model for the fully-supervised reconstruction ofthe synthetic data, guiding the reconstruction of the realistic LR imagesthrough a generative-adversarial strategy that allows the synthetic andrealistic LR images to be unified in the feature domain. Finally, a novelsparse edge-aware loss is designed to dynamically optimize the vessel edgestructure. Extensive experiments on two OCTA sets have shown that our methodperforms better than state-of-the-art super-resolution reconstruction methods.In addition, we have investigated the performance of the reconstruction resultson retina structure segmentations, which further validate the effectiveness ofour approach.

Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction