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

Published in ACM SIGGRAPH Conference on Visual Media Production (CVMP), 2022

Recommended citation: Mustafa, Aamir, Param Hanji and Rafał K. Mantiuk. "Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping." In Proceedings of the 19th ACM SIGGRAPH European Conference on Visual Media Production (CVMP). 2022. https://www.cl.cam.ac.uk/research/rainbow/projects/distil_style/paper.pdf

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.

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