  Capturing scenes with a high dynamic range is crucial to reproducing images
that appear similar to those seen by the human visual system. Despite progress
in developing data-driven deep learning approaches for converting low dynamic
range images to high dynamic range images, existing approaches are limited by
the assumption that all conversions are governed by the same nonlinear mapping.
To address this problem, we propose "Model-Agnostic Meta-Learning for HDR Image
Reconstruction" (MetaHDR), which applies meta-learning to the LDR-to-HDR
conversion problem using existing HDR datasets. Our key novelty is the
reinterpretation of LDR-to-HDR conversion scenes as independently sampled tasks
from a common LDR-to-HDR conversion task distribution. Naturally, we use a
meta-learning framework that learns a set of meta-parameters which capture the
common structure consistent across all LDR-to-HDR conversion tasks. Finally, we
perform experimentation with MetaHDR to demonstrate its capacity to tackle
challenging LDR-to-HDR image conversions. Code and pretrained models are
available at https://github.com/edwin-pan/MetaHDR.
