SI-HDR

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The dataset consists of 181 HDR images. Each image includes: 1) a RAW exposure stack, 2) an HDR image, 3) simulated camera images at two different exposures 4) Results of 6 single-image HDR reconstruction methods: Endo et al. 2017, Eilertsen et al. 2017, Marnerides et al. 2018, Lee et al. 2018, Liu et al. 2020, and Santos et al. 2020 # Project web page More details can be found at: <https://www.cl.cam.ac.uk/research/rainbow/projects/sihdr_benchmark/> # Overview This dataset contains 181 RAW exposure stacks selected to cover a wide range of image content and lighting conditions. Each scene is composed of 5 RAW exposures and merged into an HDR image using the estimator that accounts photon noise [3] (code at HDRutils). A simple color correction was applied using a reference white point and all merged HDR images were resized to 1920×1280 pixels. The primary purpose of the dataset was to compare various single image HDR (SI-HDR) methods [1]. Thus, we selected a wide variety of content covering nature, portraits, cities, indoor and outdoor, daylight and night scenes. After merging and resizing, we simulated captures by applying a custom CRF and added realistic camera noise based on estimated noise parameters of *Canon 5D Mark III*. The simulated captures were inputs to six selected SI-HDR methods. You can view the reconstructions of various methods for select scenes on our interactive viewer. For the remaining scenes, please download the appropriate zip files. We conducted a rigorous pairwise comparison experiment on these images to find that widely-used metrics did not correlate well with subjective data. We then proposed an improved evaluation protocol for SI-HDR [1]. If you find this dataset useful, please cite [1]. # References [1] Param Hanji, Rafał K. Mantiuk, Gabriel Eilertsen, Saghi Hajisharif, and Jonas Unger. 2022. “Comparison of single image hdr reconstruction methods — the caveats of quality assessment.” In *Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings (SIGGRAPH ’22 Conference Proceedings)*. [Online]. Available: <https://www.cl.cam.ac.uk/research/rainbow/projects/sihdr_benchmark/> [2] Gabriel Eilertsen, Saghi Hajisharif, Param Hanji, Apostolia Tsirikoglou, Rafał K. Mantiuk, and Jonas Unger. 2021. “How to cheat with metrics in single-image HDR reconstruction.” In *Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops*. 3998–4007. [3] Param Hanji, Fangcheng Zhong, and Rafał K. Mantiuk. 2020. “Noise-Aware Merging of High Dynamic Range Image Stacks without Camera Calibration.” In *Advances in Image Manipulation (ECCV workshop)*. Springer, 376–391. [Online]. Available: <https://www.cl.cam.ac.uk/research/rainbow/projects/noise-aware-merging/>