BI-DIRECTIONAL NORMALIZATION AND COLOR ATTENTION-GUIDED GENERATIVE ADVERSARIAL NETWORK FOR IMAGE ENHANCEMENT

Most existing image enhancement methods require paired images, and rarely consider the aesthetic quality. This paper proposes a bi-directional normalization and color attention-guided generative adversarial network (BNCAGAN) for unsupervised image enhancement. An auxiliary attention classifier (AAC) and a bi-directional normalization residual (BNR) module are designed to assist the generator in flexibly controlling the local details with the constraint from both the low/high-quality domain. Moreover, a color attention module (CAM) is proposed to preserve the color fidelity in the discriminator. The qualitative and quantitative experimental results demonstrate that our BNCAGAN is superior to the existing methods with distinctively improved authenticity and naturalness of the enhanced images. The source code is available at https://github.com/SWU-CS-MediaLab/BNCAGAN.