Image fusion and denoising using fractional-order gradient information

Image fusion and denoising are significant in image processing because of the availability of multi-sensor and the presence of the noise. The first-order and second-order gradient information have been effectively applied to deal with fusing the noiseless source images. In this paper, due to the advantage of the fraction-order derivative, we first integrate the fractional order gradients of noisy source images as the target fraction-order feature, and make it fit with the fractional-order gradient of the fused image. Then we introduce the total variation (TV) regularization for removing the noise. By adding the data fitting term between the fused image and a preprocessed image, a new convex variational model is proposed for fusing the noisy source images. Furthermore, an alternating direction method of multiplier (ADMM) is developed for solving the proposed variational model. Numerical experiments show that the proposed method outperforms the conventional total variation in methods for simultaneously fusing and denoising.