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Learning-based methods for blind single image super resolution (SISR) conductthe restoration by a learned mapping between high-resolution (HR) images andtheir low-resolution (LR) counterparts degraded with arbitrary blur kernels.However, these methods mostly require an independent step to estimate the blurkernel, leading to error accumulation between steps. We propose an end-to-endlearning framework for the blind SISR problem, which enables image restorationwithin a unified Bayesian framework with either full- or semi-supervision. Theproposed method, namely SREMN, integrates learning techniques into thegeneralized expectation-maximization (GEM) algorithm and infers HR images fromthe maximum likelihood estimation (MLE). Extensive experiments show thesuperiority of the proposed method with comparison to existing work and noveltyin semi-supervised learning.

Generalized Expectation Maximization Framework for Blind Image Super Resolution