In this paper, we propose a deep snapshot high dynamic range (HDR) imaging framework that can effectively reconstruct an HDR image from the RAW data captured using a multi-exposure color filter array (ME-CFA), which consists of a mosaic pattern of RGB filters with different exposure levels. To effectively learn the HDR image reconstruction network, we introduce the idea of luminance normalization that simultaneously enables effective loss computation and input data normalization by considering relative local contrasts in the "normalized-by-luminance" HDR domain. This idea makes it possible to equally handle the errors in both bright and dark areas regardless of absolute luminance levels, which significantly improves the visual image quality in a tone-mapped domain. Experimental results using two public HDR image datasets demonstrate that our framework outperforms other snapshot methods and produces high-quality HDR images with fewer visual artifacts.

中文翻译：

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使用多重曝光滤色镜阵列的深度快照HDR成像

在本文中，我们提出了一种深快照高动态范围（HDR）成像框架，该框架可从使用多曝光彩色滤光片阵列（ME-CFA）捕获的RAW数据有效地重建HDR图像，该图像由马赛克图案组成。具有不同曝光级别的RGB滤镜。为了有效地学习HDR图像重建网络，我们引入了亮度归一化的思想，该思想通过考虑“按亮度归一化” HDR域中的相对局部对比度，同时实现了有效的损耗计算和输入数据归一化。这种想法使得无论绝对亮度水平如何，都可以同等地处理亮区和暗区中的误差，从而显着提高了色调映射域中的可视图像质量。