  Image blending is an integral part of many multi-image applications such as
panorama stitching or remote image acquisition processes. In such scenarios,
multiple images are connected at predefined boundaries to form a larger image.
A convincing transition between these boundaries may be challenging, since each
image might have been acquired under different conditions or even by different
devices.
  We propose the first blending approach based on osmosis filters. These
drift-diffusion processes define an image evolution with a non-trivial steady
state. For our blending purposes, we explore several ways to compose drift
vector fields based on the derivatives of our input images. These vector fields
guide the evolution such that the steady state yields a convincing blended
result. Our method benefits from the well-founded theoretical results for
osmosis, which include useful invariances under multiplicative changes of the
colour values. Experiments on real-world data show that this yields better
quality than traditional gradient domain blending, especially under challenging
illumination conditions.
