Image fusion by using steerable pyramid

Liu, Zheng; Tsukada, Kazuhiko; Hanasaki, K.; Ho, Yeong-Khing; Dai, Yaping

doi:10.1016/s0167-8655(01)00047-2

Cited by 161 publications

(51 citation statements)

References 18 publications

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“…Laplacian (Burt & Adelson, 1983), ratio-of-low-pass (Toet, 1989), gradient (Burt & Kolczynski, 1993) and steerable pyramids (Liu et al, 2001), and wavelet (Forster et al, 2004;Pajares & de la Cruz, 2004;Valdecasas et al, 2001), shapelet (Meneses et al, 2008) and curvelet (Minhas et al, 2011) transforms, in order to perform high-pass filtering at different resolution level. Contrary to afore-described neighborhood-based methods, these ones thus avoid the choice of a fixed-size filter.…”

Section: Multiresolution-based Methodsmentioning

confidence: 99%

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Fernandes¹,

Gavet²,

Pinoli³

2012

Image Restoration - Recent Advances and Applications

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Section: Multiresolution-based Methodsmentioning

confidence: 99%

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Fernandes¹,

Gavet²,

Pinoli³

2012

Image Restoration - Recent Advances and Applications

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“…The space domain methods usually generate a fused image in which each space pixel is determined from a set of space pixels from the input sources, including intensity-hue-saturation (IHS) (Carper et al 1990, Chavez et al 1991, Brovey (Civco et al 1995) and principle component analysis (PCA) (Chavez andKwarteng 1989, Guo andLiu 2011). The transform domain methods convert the input images into a common transform domain, such as wavelet domain (Garguet-Duport et al 1996, Audicana et al 2004, pyramid domain (Liu et al 2001) and random transform domain (Guo and Liu 2010). Fusion is then applied by combining their transform coefficients.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Fusion Based on Discrete Fractional Random Transform for Modified IHS

Guo

Zhang

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The traditional IHS fusion, as a spatial domain method, mostly achieves the good spatial quality but the spectral distortion. The discrete fractional random transform (DFRNT) fusion, as a transform domain method, can maintain the high spectral information. Thus, in this paper, we propose a combined DFRNT-IHS approach to obtain good spectral quality and high spatial quality. Moreover, instead of fusion in spatial domain or transform domain respectively as in the conventional way, the proposed DFRNT-IHS approach is for joint spatial and transform domain. Real multi-spectral and panchromatic images are used to evaluate the effectiveness of the proposed method. The fused images are found to preserve both the spectral information of the multi-spectral image and the high spatial resolution information of the panchromatic image more effectively than several existing image fusion techniques. Spectrum distribution of DFRNT is random and dispersive, which guarantees low spectral distortion and certain robustness.

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“…Pixel-level fusion techniques can also be used to improve the efficiency of classification and detection algorithms. In general, pixel-level fusion methods can be classified into linear methods (Achalakul and Taylor 2001), nonlinear methods (Matsopoulos et al 1994, Matsopoulos and Marshall 1995, Mukhopadhyay and Chanda 2001, optimization techniques (Solberg et al 1996), neural networks (Zhang et al 2001, Shkvarko et al 2001) and image pyramids (Liu et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Multispectral image fusion for improved RGB representation based on perceptual attributes

Tsagaris

Anastassopoulos

2005

International Journal of Remote Sensing

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A pixel-level fusion technique for RGB representation of multispectral images is proposed. The technique results in highly correlated RGB components, a fact which occurs in natural colour images and is strictly related to the colour perception attributes of the human eye. Accordingly, specific properties for the covariance matrix of the final RGB image are demanded. Mutual information is employed as an objective criterion for quality refinement. The method provides dimensionality reduction, while the resulting RGB colour image is perceptually of high quality. Comparisons with existing techniques are carried out using both subjective and objective measures.

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Image fusion by using steerable pyramid

Cited by 161 publications

References 18 publications

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Remote Sensing Image Fusion Based on Discrete Fractional Random Transform for Modified IHS

Multispectral image fusion for improved RGB representation based on perceptual attributes

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