An automatic method to learn and transfer the photometric appearance of partially overlapping images
The first major contribution of this paper is a robust method to learn the photometric mapping between the overlapping portions of two registered images acquired either under different lighting conditions or different sensor modalities. Then, once such mapping is learnt, we demonstrate how it generalizes so that the photometric appearance can be transferred from one image to the other out of their overlapping area. This task is fundamental in several different contexts, such as image colorization, seamless mosaicking or change detection. After introducing the theory and discussing the algorithms, we will present several examples that confirm the efficacy of the proposed method in dealing with different types of images.