This paper presents a method to recover a spatially varying illuminant color estimate from scenes lit by multiple light sources. Starting with the image formation process, we formulate the illuminant recovery problem in a statistically data-driven setting. To do this, we use a factor graph defined across the scale space of the input image. In the graph, we utilize a set of illuminant prototypes computed using a data driven approach. As a result, our method delivers a pixelwise illuminant color estimate being devoid of libraries or user input. The use of a factor graph also allows for the illuminant estimates to be recovered making use of a maximum a posteriori inference process. Moreover, we compute the probability marginals by performing a Delaunay triangulation on our factor graph. We illustrate the utility of our method for pixelwise illuminant color recovery on widely available data sets and compare against a number of alternatives. We also show sample color correction results on real-world images.

This paper presents a method to recover the pixel-wise illuminant colour for scenes lit by multiple lights. Here, we start from the image formation process and pose the illuminant recovery task in hand into an evidence combining setting. To do this, we construct a factor graph making use of the scale space of the input image and a set of illuminant prototypes. The computation of these prototypes is data driven and, hence, our method is devoid of libraries or user input. The use of a factor graph allows for the illuminant estimates at different scales to be recovered making use of a maximum a posteriori (MAP) inference process. Moreover, we render the computation of the probability marginals used here as exact by constructing our factor graph making use of a Delaunay triangulation. We illustrate the utility of our method for pixelwise illuminant colour recovery on two widely available datasets and compare against a number of alternatives. We also show sample colour correction results on real-world images.

Here, we turn our attention to factor graphs and examine their message passing properties for image processing tasks. To this end, we focus on the maximum a posteriori (MAP) inference process in multi-layered graphs and exploit the ability of factor graphs to capture subtle interactions between image tokens, i.e. pixels, super pixels, features, etc. This leads to a general, yet simple belief propagation scheme. The benefits of doing this are two-fold. Firstly, this yields the ability to perform more accurate joint probability inference tasks at minimal additional computational cost. Secondly, we gain the advantage of modelling structural interactions between image tokens more accurately on graphical models with multiple levels of interaction (layers). We illustrate the use of factor graphs for image defogging and segmentation and compare our results against other techniques elsewhere in literature.

In this paper, we present a method to recover the albedo and depth from a single image. To this end, we depart from the scattering theory in the atmospheric vision model used elsewhere for defogging and dehazing. We then view the image as a relaxed factorial Markov random field (FMRF) of albedo and depth layers. This leads to a formulation which, for each of the layers in the FMRF, is akin to relaxation labelling problems. Moreover, we can obtain sparse representations for the graph Laplacian and Hessian matrices involved. This implies that global minima for each of the layers can be estimated efficiently via sparse Cholesky factorisation methods. We illustrate the utility of our method for depth and albedo recovery making use of real world data and compare against other techniques elsewhere in the literature.