Deep tone mapping operator for high dynamic range images

Aakanksha Rana

Abstract:

A computationally fast tone mapping operator
(TMO) that can quickly adapt to a wide spectrum of high
dynamic range (HDR) content is quintessential for visualization
on varied low dynamic range (LDR) output devices such as movie
screens or standard displays. Existing TMOs can successfully
tone-map only a limited number of HDR content and require an
extensive parameter tuning to yield the best subjective-quality
tone-mapped output. In this paper, we address this problem by
proposing a fast, parameter-free and scene-adaptable deep tone
mapping operator (DeepTMO) that yields a high-resolution and
high-subjective quality tone mapped output. Based on conditional
generative adversarial network (cGAN), DeepTMO not only
learns to adapt to vast scenic-content (e.g., outdoor, indoor,
human, structures, etc.) but also tackles the HDR related scenespecific challenges such as contrast and brightness, while preserving the fine-grained details. We explore 4 possible combinations
of Generator-Discriminator architectural designs to specifically
address some prominent issues in HDR related deep-learning
frameworks like blurring, tiling patterns and saturation artifacts.
By exploring different influences of scales, loss-functions and
normalization layers under a cGAN setting, we conclude with
adopting a multi-scale model for our task. To further leverage
on the large-scale availability of unlabeled HDR data, we train
our network by generating targets using an objective HDR quality
metric, namely Tone Mapping Image Quality Index (TMQI).
We demonstrate results both quantitatively and qualitatively,
and showcase that our DeepTMO generates high-resolution,
high-quality output images over a large spectrum of real-world
scenes. Finally, we evaluate the perceived quality of our results
by conducting a pair-wise.