Deep Image Prior for Spatio-temporal Fluorescence Microscopy Images: DECO-DIP

Image deconvolution and denoising is a common postprocessing step
to improve the quality of biomedical fluorescence microscopy images. In recent
years, this task has been increasingly tackled with the help of supervised deep
learning methods. However, generating a large number of training pairs is, if at all possible, often laborious. Here, we present a new deep learning algorithm called DECO-DIP that builds on the Deep Image Prior (DIP) framework and does not rely on training data. We extend DIP by incorporating a novel loss function that, in addition to a standard 𝐿2 data term, contains a term to model the underlying image generation forward model. We apply our framework both to synthetic data and Ca2+ microscopy data of biological samples, namely Jurkat T-cells and astrocytes. DECO-DIP outperforms both classical deconvolution and the standard DIP implementation. We further introduce an extension, DECO-DIP-T, which explicitly utilizes the time dependence in live cell microscopy image series.