Markov Random Field based Image Composition with Luminance Consistence

Directly merging a source image into a target image with different lighting condition often leads to unnatural composition result.This work proposes a luminance consistent image composition method based on Markov random field.First,the method builds a gradient maintaining contrast term based on weighted Poisson cloning method.Comparing to the traditional Poisson cloning algorithm,the proposed method easily weakens the bleeding effect in case of serious changes in luminance difference between the source and target image among the composition boundary.Second,the method builds a luminance consistent data term based on the histogram alignment method so as to make the source image's main luminance axis align with the target image.Then,the method adaptively combines two terms based on the contrast feature of the source image and the change range in luminance difference between the source and target image among the composition boundary.Finally,the method uses the Learning with Local and Global Consistency algorithm for a fast solution of the Markov random field problem.Experimental results demonstrate that the proposed method outperforms the traditional Poisson cloning method in terms of maintaining source image's gradient and luminance consistency,while achieving a faster convergence speed.