  Real-world image de-weathering aims at removing various undesirable
weather-related artifacts. Owing to the impossibility of capturing image pairs
concurrently, existing real-world de-weathering datasets often exhibit
inconsistent illumination, position, and textures between the ground-truth
images and the input degraded images, resulting in imperfect supervision. Such
non-ideal supervision negatively affects the training process of learning-based
de-weathering methods. In this work, we attempt to address the problem with a
unified solution for various inconsistencies. Specifically, inspired by
information bottleneck theory, we first develop a Consistent Label Constructor
(CLC) to generate a pseudo-label as consistent as possible with the input
degraded image while removing most weather-related degradations. In particular,
multiple adjacent frames of the current input are also fed into CLC to enhance
the pseudo-label. Then we combine the original imperfect labels and
pseudo-labels to jointly supervise the de-weathering model by the proposed
Information Allocation Strategy (IAS). During testing, only the de-weathering
model is used for inference. Experiments on two real-world de-weathering
datasets show that our method helps existing de-weathering models achieve
better performance. Codes are available at
https://github.com/1180300419/imperfect-deweathering.
