3D fluorescence deconvolution with deep priors (Conference Presentation)

The goal of this work is to incorporate Convolutional Neural Networks (CNNs) into the 3D deconvolution process without training. CNNs are well suited to the problem of 2D deconvolution, however training a CNN on 3D volumes requires excessive time and impractical amounts of training data. To circumvent these problems, we use a CNN architecture as if it were a handcrafted prior, similar to the work deep image prior. Using this method, we achieve high SSIM and PSNR metrics relative to other modern techniques for deconvolving through-focus fluorescence measurements to recover a 3D volume with no training data and minimal hyperparameter tuning.