Wavelets for Image Fusion

Nikolov, SG; Hill, Paul; Bull, David; Canagarajah, N.

doi:10.1007/978-94-015-9715-9_8

Cited by 127 publications

(94 citation statements)

References 10 publications

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“…They both retain a perceptually acceptable combination of the two "in focus" areas from each input image. An edge fusion result is also shown for comparison ( figure 3(f)) [8]. Upon closer inspection however, there are residual ringing artefacts found in the DWT fused image not found within the DT-CWT fused image.…”

Section: Qualitative Comparisonsmentioning

confidence: 94%

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Image Fusion Using Complex Wavelets

Hill¹,

Canagarajah²,

Bull³

2002

Procedings of the British Machine Vision Conference 2002

218

100

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Section: Qualitative Comparisonsmentioning

confidence: 94%

“…The maximum absolute value within a window is used as an activity measure of the central pixel of the window. A binary decision map of the same size as the 1 taken from [8] DWT is constructed to record the selection results based on a maximum selection rule.…”

Section: Discrete Wavelet Transform (Dwt) Fusionmentioning

confidence: 99%

Image Fusion Using Complex Wavelets

Hill¹,

Canagarajah²,

Bull³

2002

Procedings of the British Machine Vision Conference 2002

218

100

View full text Add to dashboard Cite

show abstract

“…Both the LPD [18] and DWT [20] based fusion methods belong to the multiscale decomposition fusion methods. They differ in the way of multiscale decomposition and the fusion rules.…”

Section: Lpd Fusion and Dwt Fusionmentioning

confidence: 99%

“…The most common procedures are methods based on simple averaging, Lap1acian pyramid method [18] and wavelet transform method [20]. In this paper, a new multimodal image fusion method based on singular value decomposition (SVD) is proposed and applied to multiple spectrum face recognition problem.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Infrared and Visible Image Fusion Algorithms for Face Recognition

Wang¹,

Liang²,

Hu³

et al. 2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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Fusion of infrared and visible image is a potential solution to improve face recognition performance. In this paper, we propose a new fusion method based on singular value decomposition (SVD) and apply it to multiple spectrum face recognition. The performance of the proposed SVD-based fusion method is compared with that of average fusion, Laplacian Pyramid Decomposition (LPD) based fusion and Discrete Wavelet Transform (DWT) based fusion methods. The performances of the four image fusion methods for face recognition are analyzed by statistical experiments and the results show that the SVD-based fusion method is better in most conditions.

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“…Additionally, the use of bivariate shrinkage functions, allows for noise reduction in the fused output, as well as the spatial and interscale dependencies of wavelet coefficients to be taken into account. As is the case with many recently proposed techniques, our developments are made using the wavelet transform, which constitutes a powerful framework for implementing image fusion algorithms [4], [6]. Specifically, methods based on multiscale decompositions consist of three main steps: first, the set of images to be fused is analysed by means of the wavelet transform, then the resulting wavelet coefficients are fused through an appropriately designed rule, and finally, the fused image is synthesized from the processed wavelet coefficients through the inverse wavelet transform.…”

Section: Introductionmentioning

confidence: 99%