Dictionary learning method for joint sparse representation-based image fusion

Zhang, Qiheng; Fu, Yuli; Li, Haifeng; Zou, Jian

doi:10.1117/1.oe.52.5.057006

Cited by 162 publications

(116 citation statements)

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“…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. Second, the performance of the NSCTSRbased method is demonstrated in comparison with the eight fusion algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…For the four sparse representation-based methods, the training set for the learned dictionary is constructed by 100,000 patches randomly selected from 50 images in Image Fusion Server; 23 the patch size and dictionary size are set as 8 × 8 and 64 × 256, which are widely used in image fusion methods. [8][9][10][11][12] We set the error tolerance ε ¼ 0.001 at sparse coding and sparsity T ¼ 10 at dictionary learning.…”

Section: Methodsmentioning

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%

“…In Ref. 12, steps (1) and (3) are both studied. During dictionary learning stage, it is implemented by joint sparse coding and singular value decomposition (SVD).…”

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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In Ref. 12, steps (1) and (3) are both studied. During dictionary learning stage, it is implemented by joint sparse coding and singular value decomposition (SVD).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image fusion with nonsubsampled contourlet transform and sparse representation

Wang

Peng²,

Feng

et al. 2013

J. Electron. Imaging

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show abstract

“…According to existing papers, objective evaluation metrics are always used to evaluate the performance of the fused image objectively [15,22,23]. Doctors actually use image information to diagnose the disease.…”

Section: Image Quality Comparisonmentioning

confidence: 99%

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Wang

Zhang

et al. 2017

Future Internet

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Abstract:Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

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