Fused variational analysis technique for high-resolution reconstruction of remote sensing imagery

In this study, we apply the robust error estimation theory as a basis to develop an appropriate procedure that performs the processing and enhancement of the remote sensing (RS) image contaminated by composite noise (additive and multiplicative) and degraded by the data acquisition system. The first reconstruction stage is performed using the Bayesian statistical estimators referred to as WCMAP { WCLS (Weighted Constrained Least Squares) and MAP (Maximum a Posteriori Probability) algorithms } which significantly increase the gain of image sharpness. Subsequently, we employ the isotropic diffusion and anisotropic diffusion methods for obtaining the optimal balance in reference to increasing the perceptual quality of RS images. The effectiveness of the proposed fused WCMAP method were evaluated through the comparative simulation of different RS image reconstructive.