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International audience; Current solutions for bacteria and viruses identification are based on time-consuming technics with complex preparation procedures. In the present work, we revealed label-free the presence of free viral particles and bacteria with a computational two-photon fluorescence (C-TPF) strategy. Six bacteria were tested: Escherichia coli, Staphylococcus epidermidis, Proteus vulgaris, Pseudomonas fluorescens, Bacillus subtilis and Clostridium perfringens. The two families of viral particles were the herpes virus with the cytomegalovirus (CMV, 300 nm of diameter) and the coronavirus with the SARS-CoV-2 (100 nm of diameter). The instrumental and computational pipeline FAMOUS optimized the produced 3D images. The origin of the fluorescence emission was discussed for bacteria regarding to their two-photon excitation spectra and attributed to the metabolic indicators (FAD and NADH). The optical and computational strategy constitute a new approach for imaging label-free viral particles and bacteria and paves the way to a new understanding of viral or bacterial ways of infection.

In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel. One of our main contributions is the integration of VBA within a neural network paradigm, following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and lead to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning., Comment: 13 pages