Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping

Many image enhancement or editing operations, such as forward and inverse tone mapping or color grading, do not have a unique solution, but instead a range of solutions, each representing a different style. Despite this, existing learning-based methods attempt to learn a unique mapping, disregarding this style. In this work, we show that information about the style can be distilled from collections of image pairs and encoded into a 2- or 3-dimensional vector. This gives us not only an efficient representation but also an interpretable latent space for editing the image style. We represent the global color mapping between a pair of images as a custom normalizing flow, conditioned on a polynomial basis of the pixel color. We show that such a network is more effective than PCA or VAE at encoding image style in low-dimensional space and lets us obtain an accuracy close to 40 dB, which is about 7-10 dB improvement over the state-of-the-art methods.Comment: Published in European Conference on Visual Media Production (CVMP '22

GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild

Most in-the-wild images are stored in Low Dynamic Range (LDR) form, servingas a partial observation of the High Dynamic Range (HDR) visual world. Despitelimited dynamic range, these LDR images are often captured with differentexposures, implicitly containing information about the underlying HDR imagedistribution. Inspired by this intuition, in this work we present, to the bestof our knowledge, the first method for learning a generative model of HDRimages 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 HDRimages which, when projected to LDR under various exposures, areindistinguishable from real LDR images. The projection from HDR to LDR isachieved via a camera model that captures the stochasticity in exposure andcamera response function. Experiments show that our method GlowGAN cansynthesize photorealistic HDR images in many challenging cases such aslandscapes, lightning, or windows, where previous supervised generative modelsproduce overexposed images. We further demonstrate the new application ofunsupervised inverse tone mapping (ITM) enabled by GlowGAN. Our ITM method doesnot need HDR images or paired multi-exposure images for training, yet itreconstructs more plausible information for overexposed regions thanstate-of-the-art supervised learning models trained on such data.<br