Presented by Asim Khwaja at Digital Image Computing: Techniques
and Applications (DICTA 2008), Canberra, Australia, 1-3 December
2008.

Abstract

An iterative algorithm for the reconstruction of natural images given only
their contrast map is presented. The solution is neuro-physiologically
inspired, where the retinal cells, for the most part, transfer only the
contrast information to the cortex, which at some stage performs
reconstruction for perception. We provide an image reconstruction
algorithm based on least squares error minimization using gradient descent
as well as its corresponding Bayesian framework for the underlying
problem. Starting from an initial image, we compute its contrast map using
the Difference of Gaussians (DoG) operator at each iteration, which is
then compared to the contrast map of the original image generating a
contrast error map. This contrast map is processed by a non-linearity to
deal with saturation effects. Pixel values are then updated proportionally
to the resulting contrast errors. Using a least squares error measure, the
result is a convex error surface with a single minimum, thus providing
consistent convergence. Our experiments show that the algorithm's
convergence is robust to initial conditions but not the performance. A
good initial estimate results in faster convergence. Finally, an extension
of the algorithm to colour images is presented. We test our algorithm on
images from the COREL public image database. The paper provides a novel
approach to manipulating an image in its contrast domain.