Fusion of multiparametric SAR images based on SW-nonsubsampled contourlet and PCNN

Yang, Shuyuan; Wang, Min; Lu, Yunfeng; Qi, Weidong; Jiao, Licheng

doi:10.1016/j.sigpro.2009.04.027

Cited by 66 publications

(26 citation statements)

References 26 publications

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“…The main drawback of these methods is the high distortion of the original spectral information present in the resulting MS images. To avoid this problem, the IHS transformation is followed by the additive wavelet or contourlet method in the so-called wavelet [91] and contourlet [99,100]additive IHS pansharpening. If the IHS transform is followed by the substitutive wavelet method, the wavelet substitutive IHS [101] pansharpening method is obtained.…”

Section: Multiresolution Familymentioning

confidence: 99%

A survey of classical methods and new trends in pansharpening of multispectral images

Amro

Mateos

Vega

et al. 2011

EURASIP J. Adv. Signal Process.

230

166

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There exist a number of satellites on different earth observation platforms, which provide multispectral images together with a panchromatic image, that is, an image containing reflectance data representative of a wide range of bands and wavelengths. Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image while simultaneously preserving its spectral information. In this paper, we provide a review of the pan-sharpening methods proposed in the literature giving a clear classification of them and a description of their main characteristics. Finally, we analyze how the quality of the pansharpened images can be assessed both visually and quantitatively and examine the different quality measures proposed for that purpose.

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Section: Multiresolution Familymentioning

confidence: 99%

A survey of classical methods and new trends in pansharpening of multispectral images

Amro

Mateos

Vega

et al. 2011

EURASIP J. Adv. Signal Process.

230

166

View full text Add to dashboard Cite

show abstract

“…But different focus measure methods are used to obtain the better quality of fused image [65]. Several researches [66][67][68][69] adopt both WT and NSCT to decompose the image, and then employ PCNN to fuse the coefficients. Yang et al [66] employ the PCNN to fuse HF coefficients, the linking strength of each neuron is determined by the clarity of coefficients.…”

Section: Nsctmentioning

confidence: 99%

“…Several researches [66][67][68][69] adopt both WT and NSCT to decompose the image, and then employ PCNN to fuse the coefficients. Yang et al [66] employ the PCNN to fuse HF coefficients, the linking strength of each neuron is determined by the clarity of coefficients. Wang et al [68] simplify the PCNN to fuse the HF coefficients, where the linking strength is determined by the average gradient of the HF coefficients, and the local variance is taken as the input to motivate the PCNN.…”

Section: Nsctmentioning

confidence: 99%

Review of Image Fusion Based on Pulse-Coupled Neural Network

Wang

Zhu

et al. 2015

Arch Computat Methods Eng

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Recently, many researchers have paid their more attention to image fusion technique based on pulse coupled neural network (PCNN). In order to make the researchers to rapidly understand the research development of image fusion based on PCNN, it is systematically reviewed in the paper. On the basis of statistical analysis on published papers, firstly, PCNN and some modified models are introduced. Then we review the PCNN's applications in the field of image fusion. Subsequently, some existing problems are summarized, while we give some suggestions for the future research.

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“…In recent years, many image fusion algorithm are applied to computer vision, pattern recognition and image processing fields such as multi-focus and multi-sensors image fusion, and so on (Xu and Chen, 2004) (Wang et al, 2008) (Qu et al, 2008). Especially, an image fusion algorithm between visible and infrared images is significant for computer vision and image processing applications.…”

Section: Introductionmentioning

confidence: 99%

“…And the PCNN has excellent performance in image edge detection applications. Recently, several image fusion algorithm based on the NSCT and PCNN have been developed, for example, based on spatial frequency-motivated PCNN in NSCT domain of Qu (Qu et al, 2008), stationary wavelet-based NSCT and PCNN of Yang (Yang et al, 2009), based on NSCT-PCNN of Ge for visible and infrared image (Ge and Li, 2010), a simplified PCNN in NSCT domain of Liu (Liu et al, 2012), and so on. These image fusion algorithm implemented better fusion performance for various image processing applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fusion Algorithm for Visible and Infrared Image using Non-subsampled Contourlet Transform and Pulse-coupled Neural Network

Ikuta

Zhang

Uwate

et al. 2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Abstract:An image fusion algorithm between visible and infrared images is significant task for computer vision applications such as multi-sensor systems. Among them, although a visible image is clear perfectly able to be seen through the naked eyes, it is often suffers with noise; while an infrared image is unclear but it has high anti-noise property. In this paper, we propose a novel image fusion algorithm for visible and infrared images using a non-subsampled contourlet transform (NSCT) and a pulse-coupled neural network (PCNN). First, we decompose two original images above mentioned into low and high frequency coefficients based on the NSCT. Moreover, each low frequency coefficients for both images are duplicated at multiple scales, and are processed by laplacian filter and average filter respectively. Finally, we can fuse the normalized coefficients by using the PCNN. Conversely, we can reconstruct a fused image based on the low and high frequency coefficients, which are fused by using the inverse NSCT. Experimental results show that the proposed image fusion algorithm surpasses the conventional and state-of-art image fusion algorithm.

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Fusion of multiparametric SAR images based on SW-nonsubsampled contourlet and PCNN

Cited by 66 publications

References 26 publications

A survey of classical methods and new trends in pansharpening of multispectral images

A survey of classical methods and new trends in pansharpening of multispectral images

Review of Image Fusion Based on Pulse-Coupled Neural Network

A Novel Fusion Algorithm for Visible and Infrared Image using Non-subsampled Contourlet Transform and Pulse-coupled Neural Network

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