Blind image decomposition aims to decompose all components present in an image, typically used to restore a multi-degraded input image. While fully recovering the clean image is appealing, in some scenarios, users might want to retain certain degradations, such as watermarks, for copyright protection. To address this need, we add controllability to the blind image decomposition process, allowing users to enter which types of degradation to remove or retain. We design an architecture named controllable blind image decomposition network. Inserted in the middle of U-Net structure, our method first decomposes the input feature maps and then recombines them according to user instructions. Advantageously, this functionality is implemented at minimal computational cost: decomposition and recombination are all parameter-free. Experimentally, our system excels in blind image decomposition tasks and can outputs partially or fully restored images that well reflect user intentions. Furthermore, we evaluate and configure different options for the network structure and loss functions. This, combined with the proposed decomposition-and-recombination method, yields an efficient and competitive system for blind image decomposition, compared with current state-of-the-art methods.

翻译：盲图像分解旨在分解图像中存在的所有成分，通常用于恢复多退化输入图像。尽管完全恢复干净图像具有吸引力，但在某些场景中，用户可能希望保留某些退化（如用于版权保护的水印）。为满足这一需求，我们在盲图像分解过程中引入可控性，允许用户指定需要移除或保留的退化类型。我们设计了一种名为可控盲图像分解网络的架构，将其嵌入U-Net结构中部。该方法首先分解输入特征图，随后根据用户指令重新组合这些特征图。其优势在于，该功能以极低计算成本实现：分解与重组均无需参数。实验表明，本系统在盲图像分解任务中表现优异，能够输出部分或完全恢复的图像，并准确反映用户意图。此外，我们评估并配置了网络结构与损失函数的不同选项。结合所提出的分解-重组方法，与当前最先进技术相比，本系统在盲图像分解中展现出高效且具有竞争力的性能。