Image reconstruction from contrast information

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.