Abstract: Image blending is an essential step in image stitching, and is often used to generate seamless blending images. However, current multi-band blending methods are much slower in processing high-resolution images. This paper presents a fast implementation of multiband blending for combining a set of registered images into a composite mosaic with no visible seams. Firstly, a unique seam image is computed 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. Secondly, the seam image and masks are compressed using run-length encoding, and all the following mask operations are built on run-length encoding scheme. Thirdly, single instruction multiple data（SIMD） instruction set is used within Laplacian pyramids construction. Finally, the seamless blending image is generated from Laplacian pyramid collapsing. Two challenging data sets are evaluated in experiment and the proposed method can composite high quality blending images efficiently. Compared with existing image blending methods, the use of run-length encoding for masks processing leads to reduced memory requirements and a compact storage of the mask data, and the use of SIMD instruction set achieves better parallelism and faster execution speed on multi-core architectures.