In this paper, we propose a framework for recovering image degradations that can be formulated by the Hadamard product of clear images with degradation factors. By training the mapping from datasets, we show that implicit feasibilities can be learned in forms of latent domains. Then with the feasibilities and acknowledged data priors, the recovery problems are formulated as a general optimization model in which the domain knowledge of degradations are also nicely involved. Then we solve the model based on the classical coordinate update with plugged-in networks so that all the variables can be well estimated. Even better, our updating scheme is designed under the guidance of theoretical analyses, thus its stability can always be guaranteed in practice. We show that different recovery problems can be solved under our unified framework, and the extensive experimental results verify that the proposed framework is superior to state-of-the-art methods in both benchmark datasets and real-world images.

在本文中，我们提出了一种恢复图像退化的框架，该框架可以由具有退化因子的清晰图像的Hadamard乘积来制定。通过训练来自数据集的映射，我们表明隐式可行性可以潜在域的形式学习。然后，利用可行性和公认的数据先验，将恢复问题公式化为通用优化模型，其中还很好地包含了降级领域的知识。然后，我们使用插入式网络对基于经典坐标更新的模型进行求解，以便可以很好地估计所有变量。更好的是，我们的更新方案是在理论分析的指导下设计的，因此在实践中始终可以保证其稳定性。我们证明了在我们统一的框架下可以解决不同的恢复问题，