Deep-learning-based super-resolution photoacoustic angiography (PAA) is a
powerful tool that restores blood vessel images from under-sampled images to
facilitate disease diagnosis. Nonetheless, due to the scarcity of training
samples, PAA super-resolution models often exhibit inadequate generalization
capabilities, particularly in the context of continuous monitoring tasks. To
address this challenge, we propose a novel approach that employs a
super-resolution PAA method trained with forged PAA images. We start by
generating realistic PAA images of human lips from hand-drawn curves using a
diffusion-based image generation model. Subsequently, we train a
self-similarity-based super-resolution model with these forged PAA images.
Experimental results show that our method outperforms the super-resolution
model trained with authentic PAA images in both original-domain and
cross-domain tests. Specially, our approach boosts the quality of
super-resolution reconstruction using the images forged by the deep learning
model, indicating that the collaboration between deep learning models can
facilitate generalization, despite limited initial dataset. This approach shows
promising potential for exploring zero-shot learning neural networks for vision
tasks.

本文介绍了一种基于深度学习的超分辨率 PAA 方法，该方法使用伪造的 PAA 图像来训练超分辨率模型，以提高其泛化性能。实验结果表明，我们的方法在原始域和交叉域测试中均优于使用真实 PAA 图像训练的模型，并展示了基于深度学习模型伪造图像的超分辨重建质量的提升，说明深度学习模型之间的协作可以促进泛化能力，尽管初始数据集有限。