Semi-Supervised Raw-to-Raw Mapping
This addresses the practical challenge of color mapping for camera manufacturers and photographers by making it more efficient, though it is incremental as it builds on prior calibration-based approaches.
The paper tackles the problem of mapping between different camera sensor raw-RGB color spaces by proposing a semi-supervised method that reduces the need for extensive paired calibration data, achieving better results than domain adaptation alternatives and a single-calibration solution.
The raw-RGB colors of a camera sensor vary due to the spectral sensitivity differences across different sensor makes and models. This paper focuses on the task of mapping between different sensor raw-RGB color spaces. Prior work addressed this problem using a pairwise calibration to achieve accurate color mapping. Although being accurate, this approach is less practical as it requires: (1) capturing pair of images by both camera devices with a color calibration object placed in each new scene; (2) accurate image alignment or manual annotation of the color calibration object. This paper aims to tackle color mapping in the raw space through a more practical setup. Specifically, we present a semi-supervised raw-to-raw mapping method trained on a small set of paired images alongside an unpaired set of images captured by each camera device. Through extensive experiments, we show that our method achieves better results compared to other domain adaptation alternatives in addition to the single-calibration solution. We have generated a new dataset of raw images from two different smartphone cameras as part of this effort. Our dataset includes unpaired and paired sets for our semi-supervised training and evaluation.