  Most of current display devices are with eight or higher bit-depth. However,
the quality of most multimedia tools cannot achieve this bit-depth standard for
the generating images. De-quantization can improve the visual quality of low
bit-depth image to display on high bit-depth screen. This paper proposes DAGAN
algorithm to perform super-resolution on image intensity resolution, which is
orthogonal to the spatial resolution, realizing photo-realistic de-quantization
via an end-to-end learning pattern. Until now, this is the first attempt to
apply Generative Adversarial Network (GAN) framework for image de-quantization.
Specifically, we propose the Dense Residual Self-attention (DenseResAtt)
module, which is consisted of dense residual blocks armed with self-attention
mechanism, to pay more attention on high-frequency information. Moreover, the
series connection of sequential DenseResAtt modules forms deep attentive
network with superior discriminative learning ability in image de-quantization,
modeling representative feature maps to recover as much useful information as
possible. In addition, due to the adversarial learning framework can reliably
produce high quality natural images, the specified content loss as well as the
adversarial loss are back-propagated to optimize the training of model. Above
all, DAGAN is able to generate the photo-realistic high bit-depth image without
banding artifacts. Experiment results on several public benchmarks prove that
the DAGAN algorithm possesses ability to achieve excellent visual effect and
satisfied quantitative performance.
