On the one hand, the dehazing task is an illposedness problem, which means that no unique solution exists. On the other hand, the dehazing task should take into account the subjective factor, which is to give the user selectable dehazed images rather than a single result. Therefore, this paper proposes a multi-output dehazing network by introducing illumination controllable ability, called IC-Dehazing. The proposed IC-Dehazing can change the illumination intensity by adjusting the factor of the illumination controllable module, which is realized based on the interpretable Retinex theory. Moreover, the backbone dehazing network of IC-Dehazing consists of a Transformer with double decoders for high-quality image restoration. Further, the prior-based loss function and unsupervised training strategy enable IC-Dehazing to complete the parameter learning process without the need for paired data. To demonstrate the effectiveness of the proposed IC-Dehazing, quantitative and qualitative experiments are conducted on image dehazing, semantic segmentation, and object detection tasks. Code is available at https://github.com/Xiaofeng-life/ICDehazing.

翻译：一方面，去雾任务属于病态问题，这意味着不存在唯一解。另一方面，去雾任务需要考虑主观因素，即向用户提供可选择的去雾图像而非单一结果。为此，本文提出一种具备照度可控能力的多输出去雾网络，称为IC-Dehazing。该网络通过调节照度可控模块的因子改变光照强度，该模块基于可解释的Retinex理论实现。IC-Dehazing的主干去雾网络采用配备双解码器的Transformer架构，可实现高质量图像复原。此外，基于先验的损失函数与无监督训练策略使IC-Dehazing无需成对数据即可完成参数学习过程。为验证所提IC-Dehazing的有效性，在图像去雾、语义分割及目标检测任务上开展了定量与定性实验。代码开源地址：https://github.com/Xiaofeng-life/ICDehazing。