基于自相似性的手绘涂鸦光声血管造影超分辨率方法
Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles
AI总结：
针对光声血管造影超分辨率模型训练样本稀缺、泛化能力不足的问题，提出用扩散模型生成的伪造PAA图像训练自相似性超分辨率模型，在原域和跨域测试中效果均优于真实图像训练的模型，为小样本下的视觉任务零样本学习提供新思路。
摘要：
基于深度学习的超分辨率光声血管造影（PAA）是一种强大的工具，可从欠采样图像中恢复血管图像，以辅助疾病诊断。然而，由于训练样本稀缺，PAA超分辨率模型往往泛化能力不足，在连续监测任务中尤为明显。为应对这一挑战，我们提出了一种新方法，采用经伪造PAA图像训练的超分辨率PAA方法。我们首先利用基于扩散的图像生成模型，从手绘曲线生成逼真的人类嘴唇PAA图像。随后，我们用这些伪造的PAA图像训练一个基于自相似性的超分辨率模型。实验结果表明，在原域和跨域测试中，我们的方法均优于用真实PAA图像训练的超分辨率模型。值得注意的是，我们的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，这表明尽管初始数据集有限，深度学习模型之间的协作仍可促进泛化。该方法为探索用于视觉任务的零样本学习神经网络展现了可观的潜力。
英文摘要：
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.
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