L2BGAN: An image enhancement model for image quality improvement and image analysis tasks without paired supervision
The paper presents an image enhancement model, L2BGAN, to translate low light images to bright images without a paired supervision. We introduce the use of geo- metric and lighting consistency along with a contextual loss criterion. These when combined with multiscale color, tex- ture and edge discriminators prove to provide competitive results. We perform extensive experiments on benchmark datasets to compare our results visually as well as objec- tively. We observe the performance of L2BGAN on real time driving datasets which are subject to motion blur, noise and other artifacts. We further demonstrate the application of image understanding tasks on our enhanced images using DarkFace and ExDark datasets.
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