Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
This addresses the challenge of accurate radiometric analysis in computer vision for applications using digital photographs, though it is incremental as it builds on existing calibration methods.
The paper tackles the problem of radiometric uncertainty in tone-mapped color images, showing that uncertainty varies across color space and proposing a model to fit this uncertainty for cameras, which provides pixel-level probability distributions over linear scene colors.
To produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range. This introduces non-linear distortion that must be undone, through a radiometric calibration process, before computer vision systems can analyze such photographs radiometrically. This paper considers the inherent uncertainty of undoing the effects of tone-mapping. We observe that this uncertainty varies substantially across color space, making some pixels more reliable than others. We introduce a model for this uncertainty and a method for fitting it to a given camera or imaging pipeline. Once fit, the model provides for each pixel in a tone-mapped digital photograph a probability distribution over linear scene colors that could have induced it. We demonstrate how these distributions can be useful for visual inference by incorporating them into estimation algorithms for a representative set of vision tasks.