  In this paper, we study two challenging and less-touched problems in single
image dehazing, namely, how to make deep learning achieve image dehazing
without training on the ground-truth clean image (unsupervised) and a image
collection (untrained). An unsupervised neural network will avoid the intensive
labor collection of hazy-clean image pairs, and an untrained model is a
``real'' single image dehazing approach which could remove haze based on only
the observed hazy image itself and no extra images is used. Motivated by the
layer disentanglement idea, we propose a novel method, called you only look
yourself (\textbf{YOLY}) which could be one of the first unsupervised and
untrained neural networks for image dehazing. In brief, YOLY employs three
jointly subnetworks to separate the observed hazy image into several latent
layers, \textit{i.e.}, scene radiance layer, transmission map layer, and
atmospheric light layer. After that, these three layers are further composed to
the hazy image in a self-supervised manner. Thanks to the unsupervised and
untrained characteristics of YOLY, our method bypasses the conventional
training paradigm of deep models on hazy-clean pairs or a large scale dataset,
thus avoids the labor-intensive data collection and the domain shift issue.
Besides, our method also provides an effective learning-based haze transfer
solution thanks to its layer disentanglement mechanism. Extensive experiments
show the promising performance of our method in image dehazing compared with 14
methods on four databases.
