The alternating least squares technique for nonuniform intensity color correction

Color correction involves mapping device RGBs to display counterparts or to corresponding XYZs. A popular methodology is to take an image of a color chart and then solve for the best 3 × 3 matrix that maps the RGBs to the corresponding known XYZs. However, this approach fails at times when the intensity of the light varies across the chart. This variation needs to be removed before estimating the correction matrix. This is typically achieved by acquiring an image of a uniform gray chart in the same location, and then dividing the color checker image by the gray-chart image. Of course, taking images of two charts doubles the complexity of color correction. In this article, we present an alternative color correction algorithm that simultaneously estimates the intensity variation and the 3 × 3 transformation matrix from a single image of a color chart. We show that the color correction problem, that is, finding the 3 × 3 correction matrix, can be solved using a simple alternating least-squares procedure. Experiments validate our approach. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 232–242, 201

Rank-based camera spectral sensitivity estimation

In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab—a difficult and lengthy procedure—or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have “raw mode.” Experiments validate our method