Learning to reconstruct images without ground-truth

Most computational imaging algorithms rely either on hand-crafted prior models (total variation, wavelets) or on supervised learning (deep neural networks) with a ground truth dataset of references. The first approach generally obtains suboptimal reconstructions, whereas the latter is impractical in many scientific and medical imaging applications, where ground-truth data is expensive or even impossible to obtain. In this talk, I will present recent algorithmic and theoretical advances in unsupervised learning for imaging inverse problems that overcome these limitations, by learning from noisy and incomplete measurement data alone. I will show how weak prior knowledge on the reconstructed image distribution, such as invariance to groups of transformations (rotations, translations, etc.) and low-dimensionality play a key role in learning from measurement data alone.