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

Abstract

Photoacoustic microscopy (PAM) has gained increasing popularity in biomedical imaging, providing new opportunities for tissue monitoring and characterization. With the development of deep learning techniques, convolutional neural networks have been used for PAM image resolution enhancement and denoising, which are two critical tasks for PAM image reconstruction and post-processing. 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 PAM 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. The generalization ability of the model is evaluated in details and experimental results on different PAM datasets demonstrate the superior performance of the method.

Keywords: photoacoustic microscopy, deep neural network, image super-resolution, image denoising