Image fusion is important in computer vision where the main goal is to integrate several sources images of the same scene into a more informative image. In this paper, we propose a variational image fusion method based on the first and second-order gradient information. Firstly, we select the target first-order and second-order gradient information from the source images by a new and simple salience criterion. Then we build our model by requiring that the first-order and second-order gradient information of the fused image match with the target gradient information, and meanwhile the fused image is close to the source images. Theoretically, we can prove that our variational model has a unique minimizer. In the numerical implementation, we take use of the split Bregman method to get an efficient algorithm. Moreover, four-direction difference scheme is proposed to discrete gradient operator, which can dramatically enhance the fusion quality. A number of experiments and comparisons with some popular existing methods demonstrate that the proposed model is promising in various image fusion applications.
We propose an adaptive regularized algorithm for remote sensing image fusion based on variational methods. In the algorithm, we integrate the inputs using a ＂grey world＂ assumption to achieve visual uniformity. We propose a fusion operator that can automatically select the total variation （TV）-LI term for edges and L2-terms for non-edges. To implement our algorithm, we use the steepest descent method to solve the corresponding Euler-Lagrange equation. Experimental results show that the proposed algorithm achieves remarkable results.
从最优化理论的角度来看,目前求解图像分割的测地线活动轮廓(geodesic active contour,GAC)模型大多采用固定步长的最速下降算法.而众所周知,该算法收敛速度较慢,这在能量泛函的梯度较小时尤为明显.对求解GAC模型的快速算法进行了研究.首先,回顾了GAC模型的演化方程;随后,将共轭梯度(conjugate gradient,CG)算法引入到GAC模型的求解中,形成一种新的求解图像分割问题的数值方法,即GAC模型的CG算法;最后,通过试验对比传统的数值方法,表明CG算法具有良好的收敛性.
