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.
|Series||DTU Compute-Technical Report-2017|
- Image fusion and denoising
- Alternating direction method of multiplier
- Inverse problem
- Fractional-order derivative
- Structure tensor