Deep Learning-Based HDR Image Upscaling Approach for 8K UHD Applications

Yixiao Wang, H. R. Tohidypour, M. Pourazad, P. Nasiopoulos, Victor C. M. Leung
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Abstract

Advances in display technology have led to the introduction of 8K Ultra High Definition (UHD) displays to the consumer market, offering an improved visual experience. However, the lack of 8K High Dynamic Range (HDR) content is a major challenge for the wide adoption. In this paper, we introduce a deep learning approach based on generative adversarial networks to generate 8K UHD HDR content from Full High Definition and 4K content. Benefiting from a multiple-level residual and dense structure, along with a random down-sampling method, our approach yields natural and visually pleasing 8K UHD HDR content with consistent color performance.
基于深度学习的8K UHD应用HDR图像升级方法
显示技术的进步导致8K超高清(UHD)显示器进入消费者市场,提供更好的视觉体验。然而,缺乏8K高动态范围(HDR)内容是广泛采用的主要挑战。在本文中,我们引入了一种基于生成对抗网络的深度学习方法,从全高清和4K内容中生成8K UHD HDR内容。得益于多层残差和密集结构,以及随机下采样方法,我们的方法产生自然且视觉上令人愉悦的8K UHD HDR内容,并具有一致的色彩表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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