Unsupervised Training of Convex Regularizers using Maximum Likelihood
  Estimation
Hong Ye Tan Ziruo Cai Marcelo Pereyra Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb
Unsupervised learning is a training approach in the situation where ground
truth data is unavailable, such as inverse imaging problems. We present an
unsupervised Bayesian training approach to learning convex neural network
regularizers using a fixed noisy dataset, based on a dual Markov chain
estimation method. Compared to classical supervised adversarial regularization
methods, where there is access to both clean images as well as unlimited to
noisy copies, we demonstrate close performance on natural image Gaussian
deconvolution and Poisson denoising tasks.