Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

Combining Component Substitution and Multiresolution Analysis: A Novel Generalized BDSD Pansharpening Algorithm

Abstract: Modern optical satellites can acquire bundles of panchSromatic (PAN) and multispectral (MS) images of the scene simultaneously. Because of the complexity of the sensors and amount of data involved, an MS image always has lower spatial resolution than the corresponding PAN image. Pansharpening aims at fusing MS images and PAN images, characterized by the spectral content of the former and the spatial details of the latter. There are two main large families of pansharpening algorithms, i.e., component substitution (CS) and multiresolution analysis (MRA). Generally speaking, the CS algorithms have better performance on spatial detail injection, while the MRA shows better spectral content preservation. In this paper, we propose a novel pansharpening algorithm, which combines the conceptions of CS and MRA. This proposed algorithm can be regarded as a generalized version of the existing band-dependent spatial-detail (BDSD) algorithm. A semisimulated dataset and three real datasets are adopted to compare the performance among the generalized-BDSD algorithm and six existing popular pansharpening algorithms. It shows that the proposed method has much lower spectral distortion and good visual appearance. In other words, the proposed method aggregates the advantages of CS and MRA, which shows effectiveness in practice.

Spline Pyramids for Inter-Modal Image Registration Using Mutual Information

Abstract: We propose a new optimizer for multiresolution image registration. It is adapted to a criterion known as mutual information and is well suited to inter-modality. Our iteration strategy is inspired by the Marquardt-Levenberg algorithm, even though the underlying problem is not least-squares. We develop a framework based on a continuous polynomial spline representation of images. Together with the use of Parzen histogram estimates, it allows for closedform expressions of the gradient and Hessian of the criterion. Tremendous simplifications result from the choice of Parzen windows satisfying the partition of unity, also based on B-splines. We use this framework to compute an image pyramid and to set our optimizer in a multiresolution context. We perform several experiments and show that it is particularly well adapted to a coarse-to-fine optimization strategy. We compare our approach to the popular Powell algorithm and conclude that our proposed optimizer is faster, at no cost in robustness or precision.

Adaptive multiresolution discontinuous Galerkin schemes for conservation laws

Abstract: A multiresolution-based adaptation concept is proposed that aims at accelerating Discontinuous Galerkin schemes applied to nonlinear hyperbolic conservation laws. Opposite to standard adaptation concepts no error estimates are needed to tag mesh elements for refinement. Instead of this, a multiresolution analysis is performed on a hierarchy of nested grids for the data given on a uniformly refined mesh. This provides difference information between successive refinement levels, that may become negligibly small in regions where the solution is locally smooth. Applying hard thresholding the data are highly compressed and local grid adaptation is triggered by the remaining significant coefficients. A central mathematical problem addressed in this work is then to show that the adaptive solution is of the same accuracy as the reference solution on a uniformly refined mesh. Numerical comparisons demonstrate the efficiency of the concept and provide reliable estimates of the actual error in the numerical solution.