In this paper, we present an unsupervised method for segmenting the illuminant regions and estimating the illumination power spectrum from a single image of a scene lit by multiple light sources. Here, illuminant region segmentation is cast as a probabilistic clustering problem in the image spectral radiance space.We formulate the problem in an optimisation setting which aims to maximise the likelihood of the image radiance with respect to a mixture model while enforcing a spatial smoothness constraint on the illuminant spectrum. We initialise the sample pixel set under each illuminant via a projection of the image radiance spectra onto a low-dimensional subspace spanned by a randomly chosen subset of spectra. Subsequently, we optimise the objective function in a coordinate-ascent manner by updating the weights of the mixture components, the sample pixel set under each illuminant and the illuminant posterior probabilities. We then estimate the illuminant power spectrum per pixel making use of these posterior probabilities. We compare our method with a number of alternatives for the tasks of illumination region segmentation, illumination colour estimation and colour correction. Our experiments show the effectiveness of our method as applied to one hyperspectral and three trichromatic image datasets.
IEEE Transactions on Image Processing (Volume:23, Issue: 8 ), Pg 3478-3489 doi: 10.1109/TIP.2014.2330768