Unsupervised Image Enhancement Via Contrastive Learning

Recent years have witnessed significant achievements for image enhancement tasks. However, many advanced algorithms are trained in a supervised manner and thus rely on a huge collection of paired data, for which the collection is itself a challenge especially for real-world scenarios. We address this issue by proposing a novel GAN framework designed for unsupervised training. To be specific, our approach introduces a contrastive loss to ensure that the content remains consistent across multiple scales in both input and output representations. In addition, we propose a multi-scale discriminator to strengthen the adversarial learning. Extensive experiments conducted in this paper showed that our algorithm achieved state-of-the-art performance on MIT-Adobe-FiveK dataset both quantitively and qualitatively.