Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkQuantizationSuper-ResolutionSimilar Papers 제목 키워드 기반
A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging
Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years. Although great progress has been achieved with the success of …
AttributeGenerative Adversarial NetworkStackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Netwo…
Generative Adversarial NetworkImage GenerationText-to-Image GenerationImage Manipulation with Natural Language using Two-sidedAttentive Conditional Generative Adversarial Network
Altering the content of an image with photo editing tools is a tedious task for an inexperienced user. Especially, when modifying the visual attributes of a specific object in an image without affecting other constituent…
Generative Adversarial NetworkImage ManipulationGenerating Photo-realistic Images from LiDAR Point Clouds with Generative Adversarial Networks
We examined the feasibility of generative adversarial networks (GANs) to generate photo-realistic images from LiDAR point clouds. For this purpose, we created a dataset of point cloud image pairs and trained the GAN to p…
ObjectObject RecognitionMegapixel Size Image Creation using Generative Adversarial Networks
Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the re…
Image Generation