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.

翻译：基于深度学习的光声血管造影（PAA）超分辨率技术是一种从欠采样图像中恢复血管图像以辅助疾病诊断的有效工具。然而，由于训练样本稀缺，PAA超分辨率模型通常表现出泛化能力不足的问题，尤其是在连续监测任务中。为解决这一挑战，我们提出了一种新方法，利用伪造的PAA图像训练超分辨率PAA模型。首先，我们通过基于扩散的图像生成模型，从手绘曲线生成逼真的人体嘴唇PAA图像；随后，使用这些伪造的PAA图像训练基于自相似性的超分辨率模型。实验结果表明，在原始领域和跨领域测试中，我们的方法均优于使用真实PAA图像训练的超分辨率模型。特别地，该方法利用深度学习模型生成的伪造图像提升了超分辨率重建质量，表明即使在初始数据集有限的情况下，深度学习模型之间的协作也能促进泛化能力。该方法为探索视觉任务中的零样本学习神经网络展现了良好的潜力。