Abstract: This paper presents a novel approach for seamless image stitching which is based on sparse feature matching and deformation propagation. First, an optimal partitioning which minimizes the structure error is found in the overlap region between the registered images, and the target region is selected from one side of partition boundary. Then, the salient structure feature is detected and matched along the partition boundary, which gets some sparse deformation vectors corresponding to the matched feature points and their associated edge points. By solving Poisson equations, these sparse deformation cues will then be propagated robustly and smoothly into the interior of the target region in which the deformation vectors of all points are derived. Finally, the gradient map of the target region is derived by interpolating the deformation vectors, from which the result is reconstructed. The implement is convenient and fast, and complex feature detection is needless. The proposed approach can handle significant structure and intensity misalignment in image stitching simultaneously, and eliminate both structure seam and intensity seam globally. Compared to several other methods, obvious improvement is achieved.