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High dynamic range (HDR) images capture much more intensity levels thanstandard ones. Current methods predominantly generate HDR images from 8-bit lowdynamic range (LDR) sRGB images that have been degraded by the cameraprocessing pipeline. However, it becomes a formidable task to retrieveextremely high dynamic range scenes from such limited bit-depth data. Unlikeexisting methods, the core idea of this work is to incorporate more informativeRaw sensor data to generate HDR images, aiming to recover scene information inhard regions (the darkest and brightest areas of an HDR scene). To this end, wepropose a model tailor-made for Raw images, harnessing the unique features ofRaw data to facilitate the Raw-to-HDR mapping. Specifically, we learn exposuremasks to separate the hard and easy regions of a high dynamic scene. Then, weintroduce two important guidances, dual intensity guidance, which guides lessinformative channels with more informative ones, and global spatial guidance,which extrapolates scene specifics over an extended spatial domain. To verifyour Raw-to-HDR approach, we collect a large Raw/HDR paired dataset for bothtraining and testing. Our empirical evaluations validate the superiority of theproposed Raw-to-HDR reconstruction model, as well as our newly captured datasetin the experiments.

RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image