Deep attentive generative adversarial network for photo-realistic image de-quantization

Most of the current display devices are with eight or higher bit-depth. However, common multimedia tools cannot achieve this bit-depth standard. Image de-quantization can improve the visual quality of low-bit-depth images for displaying on high-bit-depth screens. To achieve image de-quantization, we propose the DAGAN algorithm to perform super-resolution on image intensity resolution, which is orthogonal to the spatial resolution. DAGAN employs Generative Adversarial Networks (GANs) and achieves photo-realistic de-quantization via an end-to-end learning pattern. Our DAGAN consists of a dense residual non-local network (DRNN) and a discriminative network. We design the Dense Residual Non-local Block (DRNB) to construct DRNN. DRNB utilizes the dense network architecture to enhance the representation ability and employs the non-local module to extract features that capture long-range dependencies between pixels. Furthermore, we use the adversarial learning framework to promote our DRNN to produce high-quality natural images. Experiment results on several public benchmarks prove that our DAGAN can generate photo-realistic high-bit-depth images without quantization artifacts.