  This paper presents a novel technique for progressive online integration of
uncalibrated image sequences with substantial geometric and/or photometric
discrepancies into a single, geometrically and photometrically consistent
image. Our approach can handle large sets of images, acquired from a nearly
planar or infinitely distant scene at different resolutions in object domain
and under variable local or global illumination conditions. It allows for
efficient user guidance as its progressive nature provides a valid and
consistent reconstruction at any moment during the online refinement process.
Our approach avoids global optimization techniques, as commonly used in the
field of image refinement, and progressively incorporates new imagery into a
dynamically extendable and memory-efficient Laplacian pyramid. Our image
registration process includes a coarse homography and a local refinement stage
using optical flow. Photometric consistency is achieved by retaining the
photometric intensities given in a reference image, while it is being refined.
Globally blurred imagery and local geometric inconsistencies due to, e.g.
motion are detected and removed prior to image fusion. We demonstrate the
quality and robustness of our approach using several image and video sequences,
including handheld acquisition with mobile phones and zooming sequences with
consumer cameras.
