A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising


Inspired by contemporary PnP frameworks, we offer a new picture fusion technique. When a denoiser is used in PnP, it is considered a "black box" and plugged into an iterative method, which is formally identical to denoising an image. Flexibility and great performance can be achieved with this method, but it may be difficult to examine convergence because most state-of-the-art denoisers lack an explicit objective function. A scene-adapted denoiser (i.e., one that is tailored to the exact scene being captured) can be fed into the iterations of the alternate direction multiplier. These results are not only state-of-the-art, but they can also be proven to be correct by using this method, which is an obvious choice in picture fusion difficulties. Hyperspectral fusion/sharpening and fusion of blurred-noisy picture pairings are both examined using the proposed approach.

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