Concise Radiometric Calibration Using The Power of Ranking
This addresses a problem for machine vision algorithms and professional photographers by enabling more accurate calibration with minimal data, though it is incremental in improving existing methods.
The paper tackles the problem of radiometric calibration for cameras by introducing a rank-based method that simplifies the calibration process, requiring fewer variables and less data, and demonstrates state-of-the-art results, particularly for JPEG to raw conversion.
Compared with raw images, the more common JPEG images are less useful for machine vision algorithms and professional photographers because JPEG-sRGB does not preserve a linear relation between pixel values and the light measured from the scene. A camera is said to be radiometrically calibrated if there is a computational model which can predict how the raw linear sensor image is mapped to the corresponding rendered image (e.g. JPEGs) and vice versa. This paper begins with the observation that the rank order of pixel values are mostly preserved post colour correction. We show that this observation is the key to solving for the whole camera pipeline (colour correction, tone and gamut mapping). Our rank-based calibration method is simpler than the prior art and so is parametrised by fewer variables which, concomitantly, can be solved for using less calibration data. Another advantage is that we can derive the camera pipeline from a single pair of raw-JPEG images. Experiments demonstrate that our method delivers state-of-the-art results (especially for the most interesting case of JPEG to raw).