Adaptive fusion method of visible light and infrared images based on non-subsampled shearlet transform and fast non-negative matrix factorization

Infrared and visible image fusion scheme based on NSCT and low-level visual features

Fractional differential and variational method for image fusion and super-resolution

Infrared and multi-type images fusion algorithm based on contrast pyramid transform

Fusion of infrared-visible images using improved multi-scale top-hat transform and suitable fusion rules

Tunable-Q contourlet-based multi-sensor image fusion

Tunable-Q contourlet transform for image representation

In the same area, the terrain details described by DEM are gradually generalized and primary terrain features are only retained in the coarser-resolution DEM. In this paper, wavelet multiresolution analysis and Radical Law selection principles were combined to model the terrain generalizing processes and derive three different scale-parameter DEMs based on DEM data generated by large-scale digital topographic map. Comparison among the contour lines and elevations of different scale-parameter DEM shows smaller scale-parameter DEMs retained the features of landform morphology such as terrain outline, ridge and gully trend of large-scale DEM. Furthermore, the results show that the terrain surface is gradually smoothing and the derived DEMs still have accurate spatial structure, by analyzing the change of slope gradient, profile curvature and stream network extracted from different scale-parameter DEMs. It could model arbitrary-scale DEM using wavelet multiresolution analysis and the Radical Law.

DEM multi-scale representation based on wavelet multiresolution analysis

A rational-dilation wavelet transform (RWT) based pan-sharpening method for multi-spectral (MS) images of various oscillatory nature is proposed. The previous multi-scale transforms, such as wavelets, curvelets and contourlets, decompose an image into channels with low constant Q-factors, and aren't suitable for pan-sharpening images with different behavior in frequency domain. The RWT as an over-complete scheme not only increases the sampling in spatial and frequency domain, but also provides a tunable Q-factor approach to be suitable for a given dataset. We studied its multi-scale decomposition scheme and the RWT based pan-sharpening method. The MS image pan-sharpening experiments show that this method using a better suitable parameter set can achieve a promising performance and often outperforms many other widely-used pan-sharpening methods both in visual quality and in term of evaluation indexes.

Pan-sharpening of multi-spectral images using over-complete rational-dilation wavelet transform

We propose a wavelet transform which offers a tunable Q-factor in each decomposition level and retains the translation-invariant property as well. Rational-dilation wavelet transform, whose Q-factor could be tuned before decomposition, has a finer time-frequency localization ability than common dyadic wavelet transform. However, the fixing of its Q-factor in every decomposition level restricts the freedom of frequency-domain partition, and also the up-sampler and down-sampler in its decomposition destroys translation-invariant property. We firstly analyze the decomposition and reconstruction of a kind of rational-dilation wavelet transform, and then discuss a transform that provides a tunable Q-factor in each decomposition level and keeps the translation-invariant property. Also, this method is expanded to two-dimensional case. Finally, we show the advantages of the method in time-frequency localization via the application in DEM generalization.

Rational-dilation wavelet transform with translation invariance

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