A Variational Approach for Image Stitching II: Using Image Gradients

In [W. Wang and M. K. Ng, SIAM J. Imaging Sci., 6 (2013), pp. 1318--1344], we proposed and developed an image stitching algorithm by studying a variational model for automatically computing weighting mask functions on input images and stitching them together. The main aim of this paper is to further develop an image stitching algorithm using the gradients of input images. Our idea is to study a variational method for computing a stitched image by using an energy functional containing the data-fitting term based on the difference between the gradients of the stitched image and the input images, and the Laplacian regularization term based on the smoothness of weighting mask functions. The use of image gradient information allows us to automatically adjust the stitched image to handle color inconsistency across input images. In the model, we incorporate both boundary conditions of the stitched image and the weighting mask functions. The existence of a solution of the proposed energy functional is shown. We also present an alternating minimizing algorithm for solving the variational model numerically, and we show the convergence of this algorithm. Experimental results show that the performance of the proposed method is better than the other testing methods proposed in the literature for input images with color inconsistency.