Low-Lightgan: Low-Light Enhancement Via Advanced Generative Adversarial Network With Task-Driven Training
Guisik Kim, Dokyeong Kwon, Junseok Kwon · 2019
We propose a low-light enhancement method using an advanced generative adversarial network (GAN) and a task-driven training set. Unlike traditional training sets that only synthesize global illumination, we apply local illumination to make the training images. Furthermore, we enhance traditional GANs with spectral normalization and advanced loss functions, making training stable and leading to accurate results. Experimental results show that our method outperforms state-of-the art methods qualitatively and quantitatively and alleviates saturation problems in bright areas, which typically occur after traditional low-light enhancements.