  Getting rid of the fundamental limitations in fitting to the paired training
data, recent unsupervised low-light enhancement methods excel in adjusting
illumination and contrast of images. However, for unsupervised low light
enhancement, the remaining noise suppression issue due to the lacking of
supervision of detailed signal largely impedes the wide deployment of these
methods in real-world applications. Herein, we propose a novel
Cycle-Interactive Generative Adversarial Network (CIGAN) for unsupervised
low-light image enhancement, which is capable of not only better transferring
illumination distributions between low/normal-light images but also
manipulating detailed signals between two domains, e.g.,
suppressing/synthesizing realistic noise in the cyclic enhancement/degradation
process. In particular, the proposed low-light guided transformation
feed-forwards the features of low-light images from the generator of
enhancement GAN (eGAN) into the generator of degradation GAN (dGAN). With the
learned information of real low-light images, dGAN can synthesize more
realistic diverse illumination and contrast in low-light images. Moreover, the
feature randomized perturbation module in dGAN learns to increase the feature
randomness to produce diverse feature distributions, persuading the synthesized
low-light images to contain realistic noise. Extensive experiments demonstrate
both the superiority of the proposed method and the effectiveness of each
module in CIGAN.
