While the color image formats used by modern cameras provide visually pleasing images, they distort and discard a significant amount of signal that is useful for many applications. Existing methods for modeling physical world properties based on such narrow-gamut images use a deterministic, per-channel, one-to-one mapping to get back to wide-gamut physical scene colors, ignoring the uncertainty inherent in the process. Rather than fit a deterministic parametric model, we show that non-parametric Bayesian regression techniques, e.g. Gaussian Processes (GP), are well-suited to model this de-rendering process, and accurately capture the uncertainty in the transformation. We propose a probabilistic approach that outputs, for each low-gamut image color, a distribution over the wide-gamut scene colors that could have created it. Using a variety of different consumer camera models, we show that effective distributions can be learned by online local Gaussian process regression. Such distributions can be used to hallucinate estimates of RAW values corresponding to JPEG samples, creating “out-of-gamut” images, and also to improve robustness in related applications, e.g., when recovering three-dimensional shape via photometric stereo.
