Fast Image Blending Using Distance Transform and Run-Length Encoding

This paper presents a fast implementation of multi-band blending for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion. We first compute a unique seam image using two-pass nearest distance transform, which is independent on the order of input images and has good scalability. Each individual mask can be extracted from this seam image quickly. To promote execution speed and reduce memory usage in building large area mosaics, the seam image and masks are compressed using run-length encoding, and all the following mask operations are built on run-length encoding scheme. The use of run-length encoding for masks processing leads to reduced memory requirements and a compact storage of the mask data. We apply our fast blending system to large scale data sets and present detailed quantitative results compared with OpenCV and Enblend to demonstrate the speed improvements.