Comparison of reconstruction algorithms in compressed sensing applied to biological imaging
In this paper, we propose a short presentation of the compressed sensing imaging framework, along with a review of recent applications in the biomedical imaging field. One of the critical issue that used to hinder the application of compressed sensing in a bioimaging context is the computational cost of the underlying image reconstruction process. However, some recently published algorithms manage to overcome this difficulty, leading to acceptable reconstruction computational times. We illustrate with simulations on biological images of fluorescence microscopy a comparison of three reconstruction algorithms, evaluating data fidelity and computational efficiency.