UPAMNet: A Unified Network with Deep Knowledge Priors for Photoacoustic Microscopy

Photoacoustic microscopy (PAM) has gained increasing popularity in biomedical imaging, providing new opportunities for tissue monitoring and characterization. Resolution enhancement and denoising are two critical tasks for PAM image reconstruction and post-processing. With the development of deep learning techniques, Convolutional Neural Networks (CNN) have been used for PAM. However, there exist several inherent challenges for this approach. The available PAM datasets used for training are limited and pre-trained models with conventional pixel level constraints are error prone. This work presents a Unified PhotoAcoustic Microscopy image reconstruction Network (UPAMNet) for both image super-resolution and denoising. The proposed method takes advantage of deep image priors by incorporating three effective attention based modules and a mixed training constraint at both pixel and perception levels. With the proposed framework, transfer learning is used to mitigate the domain gaps between different datasets. The generalization ability of the model is evaluated in details and experimental results on different PAM datasets demonstrate the superior performance of the method compared to the current state-of-the-art.