Low-light Image Enhancement Based on Conditional Generative Adversarial Network

Abstract

We present an end-to-end low-light image enhancement learning method. This learning is based on the conditional generative adversarial networks(GAN) and realizes low-light images enhancement. Specifically, our method uses a convolutional neural network containing residual structures as a generator and WGAN-GP as a discriminator to generate an effective low-light enhancement model under the constraints of GAN loss, Perceptual loss and Structural similarity loss. The model can retain the detailed information of the original image, improve the brightness of the image without generating noise interference, while the generated images are more natural and have higher quality. Extensive experimental results show that our method has reached the state-of-art in multiple objective evaluation indicators of image quality, and the visual appearance is superior.