RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image
Yunhao Zou Chenggang Yan Ying Fu
High dynamic range (HDR) images capture much more intensity levels than
standard ones. Current methods predominantly generate HDR images from 8-bit low
dynamic range (LDR) sRGB images that have been degraded by the camera
processing pipeline. However, it becomes a formidable task to retrieve
extremely high dynamic range scenes from such limited bit-depth data. Unlike
existing methods, the core idea of this work is to incorporate more informative
Raw sensor data to generate HDR images, aiming to recover scene information in
hard regions (the darkest and brightest areas of an HDR scene). To this end, we
propose a model tailor-made for Raw images, harnessing the unique features of
Raw data to facilitate the Raw-to-HDR mapping. Specifically, we learn exposure
masks to separate the hard and easy regions of a high dynamic scene. Then, we
introduce two important guidances, dual intensity guidance, which guides less
informative channels with more informative ones, and global spatial guidance,
which extrapolates scene specifics over an extended spatial domain. To verify
our Raw-to-HDR approach, we collect a large Raw/HDR paired dataset for both
training and testing. Our empirical evaluations validate the superiority of the
proposed Raw-to-HDR reconstruction model, as well as our newly captured dataset
in the experiments.