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

Yang Zhang; Yunqiu Xu; Deshuai Zheng; Zedong Zhang

doi:10.1117/12.3037210

22 October 2024 Deep attentive generative adversarial network for photo-realistic image de-quantization

Yang Zhang, Yunqiu Xu, Deshuai Zheng, Zedong Zhang

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 1327413 (2024) https://doi.org/10.1117/12.3037210
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Zhang, Yunqiu Xu, Deshuai Zheng, and Zedong Zhang "Deep attentive generative adversarial network for photo-realistic image de-quantization", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 1327413 (22 October 2024); https://doi.org/10.1117/12.3037210

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Education and training

Image restoration

Gallium nitride

Image quality

Visualization

Network architectures

Convolution

Show All Keywords

Keywords/Phrases

Search In:

Publication Years