Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Abstract

High dynamic range (HDR) imaging is a technique to allow a greater dynamic range of exposures, which is a very important field in image processing, computer graphics, and vision. Recent years have witnessed a striking advancement of HDR imaging using deep learning. This paper aims to provide a systematic review and analysis of the recent development of deep HDR imaging methodologies. Overall, we hierarchically and structurally group existing deep HDR imaging methods into five categories based on the number/domain of input exposures in HDR imaging, the number of learning tasks in HDR imaging, HDR imaging using the novel sensor data, HDR imaging using novel learning strategies, and the applications. Importantly, we provide constructive discussions for each category regarding its potential and challenges. Moreover, we cover some crucial issues for deep HDR imaging, such as datasets and evaluation metrics. Lastly, we highlight some open issues and point out future directions by sharing some new perspectives.