High Dynamic Range (HDR) imaging provides the ability to capture, manipulate and display real-world lighting. This is a significant upgrade from Standard Dynamic Range (SDR) which only handles up to 255 luminance values concurrently. While capture technologies have advanced significantly over the last few years, currently available HDR capturing sensors (e.g., smartphones) only improve the dynamic range by a few stops over conventional SDR. This content has a 10-12 f-stop range, which is still substantially less than a 20 f-stop; the desired dynamic range for true HDR images. Moreover, they frequently exhibit ghosting artifacts in challenging scenes. The ability to reliably obtain high-quality HDR images remains a challenge. Furthermore, there exists a huge amount of legacy content that has been captured using SDR which needs to be adapted to be visualized on HDR displays.
The focus of this grand challenge is to transform lower range content (SDR and lower f-stops) into HDR via the process of Inverse Tone Mapping (ITM). Modern ITM operators employ deep-learning to generate HDR content. Typically, these methods recover the overall radiance and missing details in overexposed areas such as colors and high-frequency details. However, other important aspects are not taken into account such as noise levels, details/colors in underexposed areas, temporal coherency, etc.
We challenge researchers to provide a novel/improved ITM operator that improves the state-of-the-art. In this challenge, we provide a novel dataset of high-quality HDR images.
Exploring novel inverse tone mapping operators is extremely important for the industry because there is a need for high-quality conversion of legacy content shot in SDR for display on HDR displays. Furthermore, current HDR capturing technology, limited to a 10-12 f-stop range, is still not sufficient to cover all lighting conditions. Inverse tone mapping operators remain extremely important to improve the dynamic range of such content.
The registration is now open; REGISTER.
The official dataset is composed of a set of pair of images an input and the expected output. The input is a SDR image stored as a PNG file, the output is a HDR stored as HDR (Radiance format or .hdr). An example can be downloaded here.
SDR images are encoded using sRGB non-linear function; the HDR Toolbox implementation was used in this challenge; you can find it here. To obtain linear SDR values, the parameter inverse has to be set to 1.
The evaluation criteria are:
E-mail us for more information or issue to grandchallenge@isti.cnr.it.
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Alessandro Artusi |
CYENS, Cyprus |
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Francesco Banterle |
ISTI-CNR, Italy |
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Tom Bashford-Rogers |
University of the West of England, UK |
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Rémi Cozot |
University of Littoral Côte d'Opale-CNR, France |
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Kurt Debattista |
Warwick University, UK |
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Demetris Marnerides |
Independent Researcher, UK |