A unified gradient domain method for seamless image processing

Shiming Ge, Kongqiao Wang, Hao Wang, Feng Zhao

Abstract: Seamless image processing concerns stitching parts of images in a visually natural manner. In this paper, we present a seamless processing method which unifies Featuring, Optimal seam method and some recent gradient domain methods including Poisson image editing, Drag-and-drop pasting and GIST. To evaluate the processing quality visually, a variational energy function is proposed to compute both the similarity of the stitched image to each of the input images and the visibility of the seam between the stitched images. The minimum of the energy function gives a globally consistent composition in both geometrical and photometric structures. Based on 3 propositions, we study the energy function and compare it with other methods theoretically. The experimental results show the benefits of our stitching method.