Wavelet based image fusion techniques — An introduction, review and comparison

By Krista Amolins, Yun Zhang, P. Dare. 2007

603 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f8a2c8b15b5337f2ddfba9651ea65ffbafb44427

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A Critical Comparison Among Pansharpening Algorithms (2015, background): “In the same field, very appreciable results have been achieved by employing wavelet and contourlet decomposition pyramids [13], [66].”
Multi-source remote sensing data fusion: status and trends (2010, methodology): “…(CS) fusion technique (Chavez et al. 1991, Shettigara 1992, Pellemans et al. 1993, Aiazzi et al. 2007), modulation-based fusion techniques (Zhang 1999, Liu 2000, Gangkofner et al. 2008) and multi-resolution analysis (MRA)-based fusion techniques (Aiazzi et al. 2002, Amolins et al. 2007) (Figure 1).”
A survey of classical methods and new trends in pansharpening of multispectral images (2011, background): “It is important to note that undecimated, shift-invariant decompositions, and specifically “a’ trous” wavelets, where sub-band and original image pixels correspond to the same locations, produce fewer artifacts and better preserve the linear continuity of features that do not have a horizontal or ve”
A Sparse Image Fusion Algorithm With Application to Pan-Sharpening (2013, methodology): “Among the existing hundreds of various pan-sharpening methods, the most popular ones are intensity–hue–saturation technique (IHS) [2], principal component analysis (PCA) [3], Brovey transform [4], and wavelet-based fusion [5], [6].”
Assessment of image fusion procedures using entropy, image quality, and multispectral classification (2008, methodology): “A hybrid image fusion procedure that incorporates the IHS transformation into a DWT fusion procedure was recently described [46].”
A New Pan-Sharpening Method With Deep Neural Networks (2015, background): “On the contrary, multi-resolutionanalysis-based wavelet transform methods [6] can preserve good spectral information.”
Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress (2018, background): “…which is widely used in many fields, such as geophysics (Torrence and Compo 1998), plant phenology (Sakamoto et al. 2005), image processing (Amolins, Zhang, and Dare 2007), biochemical and biophysical parameters estimation of vegetation (Pu and Gong 2004; Cheng et al. 2014a; Cheng, Riaño,…”