  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.
