Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser’s repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density (i.e., undersampling) for increased imaging speed over a large field-of-view. Deep learning (DL) methods have recently been used to improve sparsely sampled PAM images; however, these methods often require time-consuming pre-training and large training dataset with ground truth. Here, we propose the use of deep image prior (DIP) to improve the image quality of undersampled PAM images. Unlike other DL approaches, DIP requires neither pre-training nor fully-sampled ground truth, enabling its flexible and fast implementation on various imaging targets. Our results have demonstrated substantial improvement in PAM images with as few as 1.4 % of the fully sampled pixels on high-speed PAM. Our approach outperforms interpolation, is competitive with pre-trained supervised DL method, and is readily translated to other high-speed, undersampling imaging modalities.

光声显微镜（PAM）是一种新兴的光与声相结合的成像方法。然而，受激光器重复率的限制，最先进的高速PAM技术常常牺牲空间采样密度(即欠采样)来提高大视场的成像速度。深度学习（DL）方法最近被用来改进稀疏采样的 PAM 图像；然而，这些方法通常需要耗时的预训练和具有地面实况的大型训练数据集。在这里，我们建议使用深度图像先验（DIP）来提高欠采样 PAM 图像的图像质量。与其他深度学习方法不同，DIP 既不需要预训练，也不需要完全采样的地面实况，从而能够在各种成像目标上灵活快速地实现。我们的结果表明，高速 PAM 上的完全采样像素少至 1.4%，PAM 图像有了显着改善。我们的方法优于插值，与预先训练的监督深度学习方法具有竞争力，并且很容易转化为其他高速、欠采样成像模式。