Image Denoising and Fusion Based on Matrix Completion

In most situations, acquired digital images always are corrupted by the wrong camera focus, serious illumination even missing data. This algorithm is presented for fusion of corrupted multi-sensor images by noise. Compared to the susceptible properties of PCA by large errors, the proposed method includes adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Robust principal components analysis via inexact augmented Lagrange multiplier method is applied to low-frequency image components, and the regional variance estimation is applied to high-frequency components including edges and details of the original image. Compared with traditional algorithms, the new algorithm not only improves the amount of preserved information and clarity, but also increases robustness for the corrupted observation data.