Pixel-level image fusion with simultaneous orthogonal matching pursuit

Yang, Bin; Li, Shutao

doi:10.1016/j.inffus.2010.04.001

Cited by 334 publications

(193 citation statements)

References 39 publications

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“…4, which fuses Laplacian Pyramid coefficients of source images by using structural similarity metric (SSIM). So we abbreviate it as LPSSIM for simplicity], and four sparse representation-based methods, i.e., SR 8 (tradition sparse representation), simultaneous orthogonal matching pursuit (SOMP), 9 joint sparse representation (JSR), 11 and method of optimal directions for joint sparse representation (MODJSR)-based fusion algorithms. 12 The parameters for different methods and evaluation metrics are first presented.…”

Section: Methodsmentioning

confidence: 99%

“…However, the sparsity of coefficients that represent the image could be increased significantly in the low-pass subbands, where approximate zero coefficients are very few, i.e., they are unable to express low-frequency information of images sparsely, while sparse representation can effectively extract the underlying information of source images. 9 If lowfrequency coefficients are integrated directly, it will degrade the performance of the fused result because the lowfrequency coefficients contain the main energy of the image.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid blocking-artifact, the step size usually is 1. [8][9][10][11][12][13] However, along with the increase of image size, the number of image blocks grows exponentially, and a great deal of calculation is needed.…”

Section: Introductionmentioning

confidence: 99%

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Image fusion with nonsubsampled contourlet transform and sparse representation

Wang

Peng²,

Feng

et al. 2013

J. Electron. Imaging

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Abstract. Image fusion combines several images of the same scene into a fused image, which contains all important information. Multiscale transform and sparse representation can solve this problem effectively. However, due to the limited number of dictionary atoms, it is difficult to provide an accurate description for image details in the sparse representation-based image fusion method, and it needs a great deal of calculations. In addition, for the multiscale transform-based method, the low-pass subband coefficients are so hard to represent sparsely that they cannot extract significant features from images. In this paper, a nonsubsampled contourlet transform (NSCT) and sparse representation-based image fusion method (NSCTSR) is proposed. NSCT is used to perform a multiscale decomposition of source images to express the details of images, and we present a dictionary learning scheme in NSCT domain, based on which we can represent low-frequency information of the image sparsely in order to extract the salient features of images. Furthermore, it can reduce the calculation cost of the fusion algorithm with sparse representation by the way of nonoverlapping blocking. The experimental results show that the proposed method outperforms both the fusion method based on single sparse representation and multiscale decompositon. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image fusion with nonsubsampled contourlet transform and sparse representation

Wang

Peng²,

Feng

et al. 2013

J. Electron. Imaging

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“…Overcomplete dictionary can be expressed more sparse image compared with the multiscale decomposition, and it can effectively extract the potential information of images and low frequency [18], but it is not easy in the fusion image edge and texture details. Therefore this paper presents a multi-scale decomposition and sparse representation algorithm combining SAR and optical images.…”

Section: Framework Of Fusion Algorithmmentioning

confidence: 99%

Fusion Algorithm OF Optical Images and SAR With SVT and Sparse Representation

Yin

2015

International Journal on Smart Sensing and Intelligent Systems

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“…If p = 0, although this non-convex functional can be solved using a pursuit algorithm [21,22], these pursuit algorithms generally require an ordering of the most influential dictionary elements for a given patch f . However, the most influential dictionary elements may vary across the multi-channel data, presenting some challenges.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion using Gaussian Mixture Models

Levine

Heaps

Koslosky

et al. 2013

Procedings of the British Machine Vision Conference 2013

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In recent years, a number of works have demonstrated that processing images using patch-based features provides more robust results than their pixel based counterparts. A contributing factor to their success is that image patches can be expressed sparsely in appropriately defined dictionaries, and these dictionaries can be tuned to a variety of applications. Yu, Sapiro and Mallat [24] demonstrated that estimating image patches from multivariate Gaussians is equivalent to finding sparse representations in a structured overcomplete PCA-based dictionary. Furthermore, their model reduces to a straightforward piecewise linear estimator (PLE). In this work we show how a similar PLE can be formulated to fuse images with various linear degradations and different levels of additive noise. Furthermore, the solution can be interpreted as a sparse patch-based representation in an appropriately defined PCA dictionary. The model can also be adapted to better preserve edges and increase PSNR by adapting the level of smoothing to each local patch.

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Pixel-level image fusion with simultaneous orthogonal matching pursuit

Cited by 334 publications

References 39 publications

Image fusion with nonsubsampled contourlet transform and sparse representation

Image fusion with nonsubsampled contourlet transform and sparse representation

Fusion Algorithm OF Optical Images and SAR With SVT and Sparse Representation

Image Fusion using Gaussian Mixture Models

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