A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising
Teodoro, A. M.
Figueiredo, M. A. T.
IEEE Transactions on Image Processing Vol. 28, Nº 1, pp. 451 - 463, January, 2019.
ISSN (print): 1057-7149
Journal Impact Factor: (in )
Digital Object Identifier: 10.1109/TIP.2018.2869727
We propose a new approach to image fusion, inspired by the recent plug-and-play (PnP) framework. In PnP, a denoiser is treated as a black box and plugged into an iterative algorithm, taking the place of the proximity operator of some convex regularizer, which is formally equivalent to a denoising operation. This approach offers flexibility and excellent performance, but convergence may be hard to analyze, as most state-of-the-art denoisers lack an explicit underlying objective function. Here, we propose using a scene-adapted denoiser (i.e., targeted to the specific scene being imaged) plugged into the iterations of the alternating direction method of multipliers. This approach, which is a natural choice for image fusion problems, not only yields state-of-the-art results but it also allows proving convergence of the resulting algorithm. The proposed method is tested on two different problems: hyperspectral fusion/sharpening and fusion of blurred-noisy image pairs.