A deraining network may be interpreted as a condition generator. Image degradation generated by the deraining network can be attributed to defective embedding features that serve as conditions. Existing image deraining methods usually ignore uncertainty-caused model errors that lower embedding quality and embed low-quality features into the model directly. In contrast, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate the advantage of the proposed method over recent state-of-the-art methods.

翻译：排减网络可被解释为一种条件生成器。排减网络产生的图像退化可归因于作为条件的有缺陷的嵌入特征。现有的图像排减方法通常忽视由不确定性引起的模型错误,这些错误导致的嵌入质量较低,并直接将低质量特征嵌入模型。相比之下,我们用潜在的高质量特征取代低质量特征。自动控制字段的闭路反馈精神被借用,以获得潜在的高质量特征。建议采用一种新的错误探测方法和特征补偿方法来解决模型错误。关于基准数据集的广泛试验以及具体的真实数据集表明拟议方法比最新最新方法的优势。