The dynamic range of our normal life can exceeds 120 dB, however, the smart-phone cameras and the conventional digital cameras can only capture a dynamic range of 90 dB, which sometimes leads to loss of details for the recorded image. Now, some professional hardware applications and image fusion algorithms have been devised to take wide dynamic range (WDR), but unfortunately existing devices cannot display WDR image. Tone mapping (TM) thus becomes an essential step for exhibiting WDR image on our ordinary screens, which convert the WDR image into low dynamic range (LDR) image. More and more researchers are focusing on this topic, and give their efforts to design an excellent tone mapping operator (TMO), showing detailed images as the same as the perception that human eyes could receive. Therefore, it is important for us to know the history, development, and trend of TM before proposing a practicable TMO. In this paper, we present a comprehensive study of the most well-known TMOs, which divides TMOs into traditional and machine learning-based category.
翻译:我们正常生活的动态范围可能超过120 dB,然而,智能手机相机和传统数字相机只能捕捉90 dB的动态范围,有时会丢失记录图像的细节。现在,一些专业硬件应用程序和图像聚合算法已经设计为采用广泛的动态范围(WDR),但不幸的是,现有设备无法显示WDR图像。因此,Tone映像(TM)成为在普通屏幕上展示WDR图像的必要步骤,这些屏幕将WDR图像转换为低动态范围(LDR)图像。越来越多的研究人员正在关注这一专题,并努力设计一个出色的音频绘图操作员(TMO),显示与人类眼睛可能得到的感知一样的详细图像。因此,在提出可行的TMO之前,我们必须了解TM的历史、发展和趋势。在本文中,我们介绍了对最著名的TMO的全面研究,它将TMO分为传统和机器学习类别。