Sparse sampling photoacoustic reconstruction with group sparse dictionary learning

Photoacoustic tomography often faces problems such as incomplete data and noise, which affect the quality of reconstructed images. Model-based photoacoustic image reconstruction is an ill-posed inverse problem, which usually needs to introduce the regularization term as the prior constraint. In this paper, we propose a novel model-based regularization framework for photoacoustic image reconstruction, which utilizes the group sparsity property of photoacoustic images as prior information and combines total variation regularization to effectively suppress image artifacts and recover the missing signal data during sparse sampling. Numerical simulation results show that the proposed algorithm not only improves the accuracy of photoacoustic reconstruction under sparse sampling but also improves the calculation speed.