We propose a state-of-the-art method for super-resolution with non-uniform blur. Single-image super-resolution methods seek to restore a high-resolution image from blurred, subsampled, and noisy measurements. Despite their impressive performance, existing techniques usually assume a uniform blur kernel. Hence, these techniques do not generalize well to the more general case of non-uniform blur. Instead, in this paper, we address the more realistic and computationally challenging case of spatially-varying blur. To this end, we first propose a fast deep plug-and-play algorithm, based on linearized ADMM splitting techniques, which can solve the super-resolution problem with spatially-varying blur. Second, we unfold our iterative algorithm into a single network and train it end-to-end. In this way, we overcome the intricacy of manually tuning the parameters involved in the optimization scheme. Our algorithm presents remarkable performance and generalizes well after a single training to a large family of spatially-varying blur kernels, noise levels and scale factors.

翻译：我们提出一种最先进的非统一模糊的超分辨率方法。 单一图像超分辨率方法试图从模糊、 子抽样和吵闹的测量中恢复高分辨率图像。 尽管其表现令人印象深刻, 现有技术通常会采用统一的模糊内核。 因此, 这些技术并不十分概括于非统一模糊的更一般情况。 相反, 我们在本文件中处理空间变换模糊的更现实和具有计算挑战性的案例。 为此, 我们首先提出一种快速的深插子算法, 其基础是线性化的ADMM 分离技术, 它可以用空间变异模糊来解决超级分辨率问题。 其次, 我们将我们的迭代算法发展到一个单一的网络, 并训练它端到端。 这样, 我们克服了手动调整优化计划所涉参数的复杂性。 我们的算法在对空间变异模糊的内核、 噪音水平和规模因素进行单一的组合培训后, 呈现出惊人的业绩和普遍化。