Image Fusion Using Complex Wavelets

Hill, Paul; Canagarajah, C N; Bull,

doi:10.5244/c.16.47

Cited by 218 publications

(104 citation statements)

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“…In the forward transform, two or more registered input images are calculated to get their wavelet coefficients. These coefficients respectively represent the approximation, horizontal, vertical and diagonal components of the input images [30]. Then, these wavelet coefficients from the different input images are combined according to certain fusion rules to get fused wavelet coefficients, after that a wavelet transform reconstruction (WTR) is applied to obtain finally a fused image (see Fig.…”

Section: Wavelet Decomposition Fusionmentioning

confidence: 99%

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Delleji¹,

Zribi²,

Hamida³

2009

TORMSJ

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Bootstrap approach and Stochastic EM algorithm combination applied for the improvement of the multisource and multi-sensor image fusion process; was presented in this research. Improvement concerned not only image quality and reducing processing execution time as mentioned in our previous Bootstrap EM algorithm (BEM), but also regarding initialization dependence as well as fixed classes' number. Such interesting fusion algorithm for multisource and multisensor image using one stochastic phase, i.e. SEM algorithm, preceded by Bootstrap procedure was successfully implemented and tested for several prototype images. Targeted images were firstly split by an unsupervised Bayesian segmentation approach in order to determine a joint region map for the fused image. The Bootstrap approach was then applied to the targeted multisource image in conjunction with the SEM algorithm, forming hence one Bootstrap SEM algorithm called BSEM. The procedure of such algorithm involved both statistical parameters' estimation from one representative Bootstrap sample of each source or sensor images.Keywords: Multi-source multi-sensor image fusion, unsupervised bayesian segmentation, bootstrap approach, sem algorithm.

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Section: Wavelet Decomposition Fusionmentioning

confidence: 99%

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Delleji¹,

Zribi²,

Hamida³

2009

TORMSJ

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“…The development of fusion algorithms using various kinds of pyramid/wavelet transforms has led to numerous pixel-and feature-based fusion methods [18][19][20][21][22][23][24]. The motivation for the pyramid/wavelet-based methods emerges from observations that the human visual system is primarily sensitive to local contrast changes, for example, edges and corners.…”

Section: Introductionmentioning

confidence: 99%

Spatial, Temporal, and Interchannel Image Data Fusion for Long‐Distance Terrestrial Observation Systems

Fishbain

Yaroslavsky

Ideses

2008

Advances in Optical Technologies

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This paper presents methods for intrachannel and interchannel fusion of thermal and visual sensors used in long-distance terrestrial observation systems. Intrachannel spatial and temporal fusion mechanisms used for image stabilization, super-resolution, denoising, and deblurring are supplemented by interchannel data fusion of visual-and thermal-range channels for generating fused videos intended for visual analysis by a human operator. Tests on synthetic, as well as on real-life, video sequences have confirmed the potential of the suggested methods.

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“…Image fusion is the combination of two or more source images which vary in resolution, instrument modality, or image capture technique into a single composite representation (Hill et al, 2002). Thus, the source images are complementary in many ways, with no one input image being an adequate data representation of the scene.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion Using a Parameterized Logarithmic Image Processing Framework

Agaian¹,

Panetta²,

Nercessian³

2011

Image Fusion

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Image Fusion Using Complex Wavelets

Abstract: The fusion of images is the process of combining two or more images into

Cited by 218 publications

References 10 publications

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Spatial, Temporal, and Interchannel Image Data Fusion for Long‐Distance Terrestrial Observation Systems

Image Fusion Using a Parameterized Logarithmic Image Processing Framework

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