Image fusion based on a new contourlet packet

Yang, Shuyuan; Wang, Min; Jiao, Licheng; Wu, Ruixia; Wang, Zhaoxia

doi:10.1016/j.inffus.2009.05.001

Cited by 198 publications

(76 citation statements)

References 12 publications

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“…Novel multi-scale geometric tools like curvelet transform [7], contourlet transform are used for the representation of spatial structures [8]. Points discontinuities are detected by using Laplacian pyramid in the contourlet transform [9]. To create bond between point discontinuities and linear structures, Directional filter bank is used.…”

Section: (A) Multi-scale Decomposition Based Methodsmentioning

confidence: 99%

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

Elaiyaraja¹,

Kumar²

2018

IJET

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Image Processing is an art to get an enriched image or it can be used to retrieve information. This image processing methods are used in medical field also. Numerous modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) etc. are used to analyze and diagnose diseases.Pixel-level image fusion is a combination of several images collected from various inputs and gives more information than any other input messages. Pixel-level image fusion shows a vital role in medical imaging. In this paper, pixel-level image fusionsmethods are survived and review the fusion quality measures are being used. Finally this surveycomplete with different kinds of image fusion methods proposed and still there are so many imminent ways in image fusion applications. Hence image fusions fields are pointedly develop in the forthcoming years.

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Section: (A) Multi-scale Decomposition Based Methodsmentioning

confidence: 99%

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

Elaiyaraja¹,

Kumar²

2018

IJET

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“…In 2010, Yang et al [43] proposed a new contourlet packet and implemented the image fusion based on PCNN in this transform domain. In fact, PCNN is taken as an existing tool to fuse coefficients.…”

Section: Ctmentioning

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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“…A popular class of algorithms are the multi-scale image fusion schemes, which decompose the source images into spatial primitives at multiple spatial scales, then integrate these primitives to form a new ('fused') multi-scale representation, and finally apply an inverse multi-scale transform to reconstruct the fused image. Examples of this approach are for instance the Laplacian pyramid (Burt & Adelson, 1983), the Ratio of Low-Pass pyramid (Toet, 1989b), the contrast pyramid (Toet, Van Ruyven & Valeton, 1989), the filter-subtract-decimate Laplacian pyramid (Burt, 1988;Burt & Kolczynski, 1993), the gradient pyramid (Burt, 1992;Burt & Kolczynski, 1993), the morphological pyramid (Toet, 1989a), the discrete wavelet transform (Lemeshewsky, 1999;Li, Manjunath & Mitra, 1995;Li, Kwok & Wang, 2002;Scheunders & De Backer, 2001), the shift invariant discrete wavelet transform (Lemeshewsky, 1999;Rockinger, 1997;Rockinger, 1999;Rockinger & Fechner, 1998), the contourlet (Yang et al, 2010), the shift-invariant shearlet transform (Wang, Li & Tian, 2014), the nonsubsampled shearlet transform (Kong, Wang & Lei, 2015;Liu et al, 2016;Zhang et al, 2015), the ridgelet transform (Tao, Junping & Ye, 2005). The filters applied in several of the earlier techniques typically produce halo artefacts near edges.…”

Section: Introductionmentioning

confidence: 99%

Iterative guided image fusion

Toet¹

2016

PeerJ Computer Science

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We propose a multi-scale image fusion scheme based on guided filtering. Guided filtering can effectively reduce noise while preserving detail boundaries. When applied in an iterative mode, guided filtering selectively eliminates small scale details while restoring larger scale edges. The proposed multi-scale image fusion scheme achieves spatial consistency by using guided filtering both at the decomposition and at the recombination stage of the multi-scale fusion process. First, size-selective iterative guided filtering is applied to decompose the source images into approximation and residual layers at multiple spatial scales. Then, frequency-tuned filtering is used to compute saliency maps at successive spatial scales. Next, at each spatial scale binary weighting maps are obtained as the pixelwise maximum of corresponding source saliency maps. Guided filtering of the binary weighting maps with their corresponding source images as guidance images serves to reduce noise and to restore spatial consistency. The final fused image is obtained as the weighted recombination of the individual residual layers and the mean of the approximation layers at the coarsest spatial scale. Application to multiband visual (intensified) and thermal infrared imagery demonstrates that the proposed method obtains state-of-the-art performance for the fusion of multispectral nightvision images. The method has a simple implementation and is computationally efficient. Subjects Computer Vision

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Image fusion based on a new contourlet packet

Cited by 198 publications

References 12 publications

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

Review of Image Fusion Based on Pulse-Coupled Neural Network

Iterative guided image fusion

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