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Abstract

Most in-the-wild images are stored in Low Dynamic Range (LDR) form, serving
as a partial observation of the High Dynamic Range (HDR) visual world. Despite
limited dynamic range, these LDR images are often captured with different
exposures, implicitly containing information about the underlying HDR image
distribution. Inspired by this intuition, in this work we present, to the best
of our knowledge, the first method for learning a generative model of HDR
images from in-the-wild LDR image collections in a fully unsupervised manner.
The key idea is to train a generative adversarial network (GAN) to generate HDR
images which, when projected to LDR under various exposures, are
indistinguishable from real LDR images. The projection from HDR to LDR is
achieved via a camera model that captures the stochasticity in exposure and
camera response function. Experiments show that our method GlowGAN can
synthesize photorealistic HDR images in many challenging cases such as
landscapes, lightning, or windows, where previous supervised generative models
produce overexposed images. We further demonstrate the new application of
unsupervised inverse tone mapping (ITM) enabled by GlowGAN. Our ITM method does
not need HDR images or paired multi-exposure images for training, yet it
reconstructs more plausible information for overexposed regions than
state-of-the-art supervised learning models trained on such data.