Illumination Controllable Dehazing Network based on Unsupervised Retinex
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Jie Gui Xiaofeng Cong Lei He Yuan Yan Tang James Tin-Yau Kwok
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.