Strong and Controllable Blind Image Decomposition
Zeyu Zhang Junlin Han Chenhui Gou Hongdong Li Liang Zheng
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.