Self-Supervised High Dynamic Range Imaging: What Can Be Learned from a Single 8-bit Video?

IF 7.8 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Francesco Banterle, Demetris Marnerides, Thomas Bashford-Rogers, Kurt Debattista
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引用次数: 0

Abstract

Recently, Deep Learning-based methods for inverse tone mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. To be effective, deep learning-based methods need to learn from large datasets and transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR 8-bit video? With the presented self-supervised approach, we show that, in many cases, a single SDR video is sufficient to generate an HDR video of the same quality or better than other state-of-the-art methods.

自监督高动态范围成像:从单个 8 位视频中能学到什么?
最近,基于深度学习的反色调映射标准动态范围(SDR)图像以获得高动态范围(HDR)图像的方法变得非常流行。这些方法能够在细节和动态范围方面令人信服地填补曝光过度的区域。要想取得成效,基于深度学习的方法需要从大型数据集中学习,并将这些知识转移到网络权重中。在这项工作中,我们从一个完全不同的角度来解决这个问题。我们能从单个 SDR 8 位视频中学到什么?我们提出的自监督方法表明,在许多情况下,单个 SDR 视频足以生成与其他先进方法质量相同或更好的 HDR 视频。
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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来源期刊
ACM Transactions on Graphics
ACM Transactions on Graphics 工程技术-计算机:软件工程
CiteScore
14.30
自引率
25.80%
发文量
193
审稿时长
12 months
期刊介绍: ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal that aims to disseminate the latest findings of note in the field of computer graphics. It has been published since 1982 by the Association for Computing Machinery. Starting in 2003, all papers accepted for presentation at the annual SIGGRAPH conference are printed in a special summer issue of the journal.
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