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

翻译：基于学习的方法通过高分辨率（HR）图像与其因任意模糊核退化后的低分辨率（LR）对应图像之间的学习映射来执行盲单图像超分辨（SISR）重建。然而，这些方法大多需要独立的步骤来估计模糊核，从而导致步骤间的误差累积。我们针对盲SISR问题提出了一种端到端学习框架，能够在统一贝叶斯框架下实现全监督或半监督的图像重建。所提出的方法SREMN将学习技术集成到广义期望最大化（GEM）算法中，并通过最大似然估计（MLE）推断HR图像。大量实验表明，与现有工作相比，该方法具有优越性，并在半监督学习中展现出新颖性。