A Scene-Adapted Gaussian-Mixture-Based Denoising Algorithm for Convergent Image Fusion PROJECT TITLE : A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising ABSTRACT: 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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Explicit Coherence in a Continuous Random Walk Model Image Segmentation Regularization From Multiple Descriptions, a Convex Optimization Framework for Video Quality and Resolution Enhancement
