Ni-DehazeNet: representation learning via bilevel optimized architecture search for nighttime dehazing.

Nighttime dehazing is a challenging ill-posed problem due to the severe haze pollution and color attenuation. Since available daytime dehazing approaches cannot be consistently adapted to the nighttime case, this paper specifically designs representation learning to enhance an end-to-end nighttime dehazing network. To explore attentive and problem-driven representations, we propose to realize effective learning via knowledge distillation together with neural architecture search (NAS), which are implemented via two parallel branches of networks, namely, representation learning network (RLN) and guided dehazing network (GDN). The challenges in nighttime dehazing come from distilling the clear elements from the hazy representation and the restoration of real color from ambient illumination or low light condition. To this end, we introduce the subnetwork of RLN to learn the feature of the clear elements of the ground truth. We specifically introduce a NAS for exploring the color subspace of the latent embedding space of both RLN and GDN. With the guidance from the representation learned by RLN, GDN could well learn the clear elements from the hazy image and recover the real color of it effectively. Extensive experimental results on the synthetic and real-world datasets demonstrate the superiority of our method over the state-of-the-arts in nighttime dehazing. [ABSTRACT FROM AUTHOR]