NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
It addresses the problem of efficient and high-fidelity HDR imaging for computer vision researchers, but is incremental as it builds on existing challenge frameworks.
This paper reviews the NTIRE 2022 challenge on constrained high dynamic range imaging, which tackled the problem of estimating HDR images from multiple low dynamic range observations under fidelity and complexity constraints, with results including top-performing methods achieving PSNR scores up to 48.5 dB in Track 1 and complexity reductions of over 50% in Track 2.
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from 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 of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).