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.
|Title of host publication||Proceedings of the 30th ACM International Conference on Multimedia|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||9|
|Publication status||Published - 10 Oct 2022|
|Event||The 30th ACM International Conference on Multimedia - Lisbon, Portugal|
Duration: 10 Oct 2022 → 14 Oct 2022
|Conference||The 30th ACM International Conference on Multimedia|
|Period||10/10/22 → 14/10/22|
Bibliographical noteThis work was supported in part by National Natural Science Foundation of China under Grant 61976159, Shanghai Innovation Action Project of Science and Technology under Grant 20511100700.
- generative adversarial network (GAN)
- low-light image enhancement
- quality attention module