NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

Eduardo Perez-Pellitero, Sibi Catley-Chandar, Ales Leonardis, Radu Timofte; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 691-700

Abstract

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021. This manuscript focuses on the newly introduced dataset, the proposed methods and their results. The challenge aims at estimating a HDR image from one or multiple respective low-dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed by two tracks: In Track 1 only a single LDR image is provided as input, whereas in Track 2 three differently-exposed LDR images with inter-frame motion are available. In both tracks, the ultimate goal is to achieve the best objective HDR reconstruction in terms of PSNR with respect to a ground-truth image, evaluated both directly and with a canonical tonemapping operation.

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[bibtex]
@InProceedings{Perez-Pellitero_2021_CVPR, author = {Perez-Pellitero, Eduardo and Catley-Chandar, Sibi and Leonardis, Ales and Timofte, Radu}, title = {NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {691-700} }