We challenge researchers to provide a novel ITM operator that improves the state-of-the-art!

As part of the IEEE ICIP international conference, Dr Alessandro Artusi (CYENS DeepCamera Research Group Leader) is co-organizing a challenge to transform lower range content (SDR and lower f-stops) into HDR via the process of Inverse Tone Mapping (ITM).

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 ITM operator that improves the state-of-the-art. In this challenge, we provide a novel dataset of high-quality HDR images.

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