Reference-based OCT Angiogram Super-resolution with Learnable Texture
  Generation
Yuyan Ruan Dawei Yang Ziqi Tang An Ran Ran Carol Y. Cheung Hao Chen
Optical coherence tomography angiography (OCTA) is a new imaging modality to
visualize retinal microvasculature and has been readily adopted in clinics.
High-resolution OCT angiograms are important to qualitatively and
quantitatively identify potential biomarkers for different retinal diseases
accurately. However, one significant problem of OCTA is the inevitable decrease
in resolution when increasing the field-of-view given a fixed acquisition time.
To address this issue, we propose a novel reference-based super-resolution
(RefSR) framework to preserve the resolution of the OCT angiograms while
increasing the scanning area. Specifically, textures from the normal RefSR
pipeline are used to train a learnable texture generator (LTG), which is
designed to generate textures according to the input. The key difference
between the proposed method and traditional RefSR models is that the textures
used during inference are generated by the LTG instead of being searched from a
single reference image. Since the LTG is optimized throughout the whole
training process, the available texture space is significantly enlarged and no
longer limited to a single reference image, but extends to all textures
contained in the training samples. Moreover, our proposed LTGNet does not
require a reference image at the inference phase, thereby becoming invulnerable
to the selection of the reference image. Both experimental and visual results
show that LTGNet has superior performance and robustness over state-of-the-art
methods, indicating good reliability and promise in real-life deployment. The
source code will be made available upon acceptance.