Based on the variational idea, we propose a new fusion strategy for nonsubsampled contourlet transform (NSCT). For
NSCT bandpass subband coefficients of input images, we take the main component of coefficients as the target and then
build an extremum problem for energy functional to find the closest to the target one as the fused coefficient. We apply
the gradient descent flow to minimize the functional and give the numerical scheme. The experimental results show that
the proposed strategy outperforms state-of-the-art image fusion strategies for NSCT in terms of both visual quality and
objective evaluation criteria.
According to the characteristics of MODIS data stripe noises, we propose a novel variational method for stripe noise
reduction. First we find the detectors contaminated by stripe noises by separating MODIS data into several subimages
due to the numbers of scan detectors. Then for subimages with stripe noises, we build an energy minimization problem
by combining two energy terms to find the solution as the destriped result. The first energy term uses variational
histogram matching method to remove detector-to-detector stripes and mirror side stripes while the second energy term
uses non-linear anisotropic diffusion method to remove the random noise of noisy stripes. The gradient descent flow is
applied to minimize the total energy functional and the numerical scheme is presented. Experimental results show that
the method can reduce stripes noises effectively.
In this paper, according to the statistical characteristics of SAR images, we develop a new speckle reduction method
based on directionlet transform by constructing a local texture-direction detector to adapt to the textural properties of the
scene. Before despeckling, direction of texture is calculated according to the edge map. We take the standard deviation
as the criterion while eight different directions arc considered. After directionlet transform, a Bayesian estimator is then
applied to the decomposed data to estimate the best value for the noise-free coefficients. The denoising performance is
among the state-of-the-art techniques based on standard wavelet transform.