Perceptual-based hyperspectral image fusion using multiresolution analysis

Wilson, Terry A.; Rogers, Steven K.; Myers, Lemuel R.

doi:10.1117/12.213617

Cited by 95 publications

(49 citation statements)

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“…In most of the fusion rules the low frequency coefficient didn't get attention and obtained by just averaging operation. It may decrease the contrast of fused image [20]. In the present paper, we have proposed a new fusion rule that avoids the problems of earlier proposed rules.…”

Section: Introductionmentioning

confidence: 99%

Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization

Srivastava¹,

Khare²

2015

IJIGSP

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Abstract-Image fusion is a popular application of image processing which performs merging of two or more images into one. The merged image is of improved visual quality and carries more information content. The present work introduces a new image fusion method in complex wavelet domain. The proposed fusion rule is based on a level dependent threshold, where absolute difference of a wavelet coefficient from the threshold value is taken as fusion criteria. This absolute difference represents variation in the image intensity that resembles the salient features of image. Hence, for fusion, the coefficients that are far from threshold value are being selected. The motivation behind using dual tree complex wavelet transform is due to failure of real valued wavelet transform in many aspects. Good directional selectivity, availability of phase information and approximate shift invariant nature of dual tree complex wavelet transform make it suitable for image fusion and help to produce a high quality fused image. To prove the strength of the proposed method, it has been compared with several spatial, pyramidal, wavelet and new generation wavelet based fusion methods. The experimental results show that the proposed method outperforms all the other state-ofthe-art methods visually as well quantitatively as in terms of standard deviation, mutual information, edge strength, fusion factor, sharpness and average gradient.Index Terms-Image fusion, dual tree complex wavelet transform, level dependent threshold, threshold based fusion, multiresolution analysis.

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Section: Introductionmentioning

confidence: 99%

Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization

Srivastava¹,

Khare²

2015

IJIGSP

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“…The most common form of transform image fusion is wavelet transform fusion [1,2,4,6,9]. In common with all transform domain fusion techniques the transformed images are combined in the transform domain using a defined fusion rule then transformed back to the spatial domain to give the resulting fused image.…”

Section: Wavelet Transform Fusionmentioning

confidence: 99%

“…Rather than using a binary decision, the resulting coefficients are given by a weighted average based on the local activity levels in each of the images' subbands. Another method called contrast sensitivity fusion is given in [9]. This method uses a weighted energy in the human perceptual domain, where the perceptual domain is based upon the frequency response, i.e.…”

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

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“…The increasing availability and the dramatic cost-reduction of co-registered multimodal imagery from different types of sensors has spurred the development of techniques for image fusion 1,9,13,[21][22][23]25,28,29,31 . The goal of image fusion is to represent the visual information present in any number of input images in a single fused image, without the introduction of distortion and artifacts or loss of information.…”

Section: Introductionmentioning

confidence: 99%

<title>Performance comparison of different gray-level image fusion schemes through a universal image quality index</title>

Toet

Hogervorst

2003

Signal Processing, Sensor Fusion, and Target Recognition XII

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We applied a recently introduced universal image quality index Q that quantifies the distortion of a processed image relative to its original version, to assess the performance of different graylevel image fusion schemes. The method is as follows. First, we adopt an original test image as the reference image. Second, we produce several distorted versions of this reference image. The distortions in the individual images are complementary, meaning that the same distortion should not occur at the same location in all images (it should be absent in at least one image). Thus, the information content of the overall set of distorted images should equal the information content of the original test image. Third, we apply the image fusion process to the set of distorted images. Fourth, we quantify the similarity of the fused image to the reference image by computing the universal image quality index Q. The method can also be used to optimize image fusion schemes for different types of distortions, by maximizing Q through repeated application of steps two and three for different parameter settings of the fusion scheme.

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Perceptual-based hyperspectral image fusion using multiresolution analysis

Cited by 95 publications

References 0 publications

Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization

Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization

Image Fusion Using Complex Wavelets

<title>Performance comparison of different gray-level image fusion schemes through a universal image quality index</title>

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