Correcting color and hyperspectral images with identification of distortion model

This paper presents a novel identification-based image correction method using a bi-illuminant dichromatic reflection model. Image patches with uniform properties over distorted and distortion-free images or image parts are used as a prior knowledge for identification. We identify the distortion correction function on a set of these patches, called spectrum shape elements, with the Hausdorff metric. The main issue during prior knowledge representation is for each distorted spectrum shape element to find a corresponding distortion-free element. A necessary condition to find a matching spectrum shape element is presented and theoretically proved. Identification problem was solved using a RANSAC-based optimization with this necessary condition as an optimization constraint. The method works well both for color and hyperspectral images. The proposed image correction procedure was tested on a set of color images and AVIRIS hyperspectral remote sensing data and proved to provide the quality superior to the results obtained with Retinex correction.