Perceptual Image Enhancement by Relativistic Discriminant Learning with Cross-Scale Aggregated Representation

Abstract

In this paper, we introduce an automatic method for image quality enhancement. Our method relies on a multi-level connected generator and a pair-wise relativistic discriminator. Moreover, we also incorporate auxiliary loss functions to exhibit a high-quality image enhancement. Different from other image style transfer approaches, our method has two appealing properties: 1) different scale information not only pass on the same scale but also flow from encoder to decoder freely and 2) more contextual information is provided to help the discriminator to perform pair-wise discriminative learning. The learned generative model can then automatically enhance the low-quality image into a digital single lens reflex (DSLR) quality. The extensive experiments demonstrate that the proposed framework surpasses the state-of-the-art methods with a clear margin.