Estimating camera response functions using probabilistic intensity similarity

We propose a method for estimating camera response functions using a probabilistic intensity similarity measure. The similarity measure represents the likelihood of two intensity observations corresponding to the same scene radiance in the presence of noise. We show that the response function and the intensity similarity measure are strongly related. Our method requires several input images of a static scene taken from the same viewing position with fixed camera parameters. Noise causes pixel values at the same pixel coordinate to vary in these images, even though they measure the same scene radiance. We use these fluctuations to estimate the response function by maximizing the intensity similarity function for all pixels. Unlike prior noise-based estimation methods, our method requires only a small number of images, so it works with digital cameras as well as video cameras. Moreover, our method does not rely on any special image processing or statistical prior models. Real-world experiments using different cameras demonstrate the effectiveness of the technique.