HDR Reconstruction from a Single Raw Image
Fig.1. Representative examples for Raw-to-HDR dataset SRHDR (Single Raw HDR). For each scene, a raw image captured under challenging lighting is served as input image, and the HDR image that is merged by bracket exposures are used as ground truth.
HDR Reconstruction from a Single Raw Image
The dynamic range of real-world scenes frequently exceeds the capture capabilities of standard consumer camera sensors, often resulting in loss of detail in both overly bright and dark areas. To address this, the computational imaging community has extensively explored High Dynamic Range (HDR) imaging, which records a broader spectrum of intensity levels and captures more scene information. Unlike conventional Low Dynamic Range (LDR) images, HDR preserves greater detail in both over- and under-exposed areas. This enhancement not only benefits various vision tasks, such as segmentation and object detection but also produces more visually pleasing images — a goal long pursued by computer vision researchers.
To further push the research of HDR reconstruction forward, we are launching a challenge centered on reconstructing HDR images from single raw images. This approach specifically focus on single raw image HDR reconstruction, which avoids potential misalignments that can occur in multi-image fusion. We will use the Raw-to-HDR dataset called SRHDR, mainly curated by Prof. Fu’s team in [a]. This dataset contains paires of LDR and HDR images. The LDR input is captured under challenging lighting conditions, representing the over- and under-exposed regions of a high dynamic range scene. The corresponding ground truth HDR images in the dataset are produced through bracketed exposures of each scene, subsequently merged using basic HDR fusion algorithms (Debevec etal., 2008). We will host the competition using open source online platform, e.g. CodaLab. All submissions are evaluated by our script running on the server and we will double check the results of top-rank methods manually before releasing the final test-set rating.