Dynamic Infrared and Visible Image Sequence Fusion Based on DT-CWT Using GGD

Zhang, Xiuqiong; Gao, Zhisheng; Yuan, Huipin

doi:10.1109/iccsit.2008.28

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…In this section, to illustrate the effectiveness of the proposed approach, we compared our algorithm with several other state-of-the-art fusion approaches. These comparative methods are: GGD-based fusion method (GGD) 27 and Bivariate-Laplace-based (BLP) 11 fusion method. For all the methods, we adopt weighted-average scheme based on coefficient intensity to fuse low-frequency sub-band coefficients.…”

Section: Methodsmentioning

confidence: 99%

“…In order to evaluate the performance of the noisy image fusion methods, the input images have been additionally corrupted by Gaussian white noise, with standard deviation σ n of 5 and 10. Two algorithms adopted for result comparison are the same as the algorithms in Section 3, these comparative methods are: GGD 27 BLP. 11 The former was proposed for fusion of noise-free images, in order to employ this algorithm in fusion of noisy image, bi-shrinkage was adopted to remove noise as preprocessing, and its parameters of bi-shrinkage were kept the same as the other algorithms.…”

Section: Fusion Of Noisy Imagesmentioning

confidence: 99%

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Fusion of noisy images based on joint distribution model in dual‐tree complex wavelet domain

Sun

Zhu

Luo

et al. 2018

Int J Imaging Syst Tech

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Source images are frequently corrupted by noise before fusion, which will lead to the quality decline of fused image and the inconvenience for subsequent observation. However, at present, most of the traditional medical image fusion scheme cannot be implemented in noisy environment. Besides, the existing fusion methods scarcely make full use of the dependencies between source images. In this research, a novel fusion algorithm based on the statistical properties of wavelet coefficients is proposed, which incorporates fusion and denoising simultaneously. In the proposed algorithm, the new saliency and matching measures are defined by two distributions: the marginal statistical distribution of single wavelet coefficient fit by the generalized Gaussian Distribution and joint distribution of dual source wavelet coefficients modeled by the anisotropic bivariate Laplacian model. Additionally, the bivariate shrinkage is introduced to develop a noise robust fusion method, and a moment-based parameter estimation applied in the fusion scheme is also exploited in denoising method, which allows to achieve the consistency of fusion and denoising. The experiments demonstrate that the proposed algorithm performs very well on both noisy and noise-free images from multimodal medical datasets (computerized tomography, magnetic resonance imaging, magnetic resonance angiography, etc.), outperforming the conventional methods in terms of both fusion quality and noise reduction. K E Y W O R D Sbi-shrinkage, image fusion, KL-divergence, mutual information, statistical distribution 1 | INTRODUCTION Imagology plays an important role in medical science, owing to the application for the visualization of anatomy structure, function, and metabolism information. 1 Actually, singular modality medical images exhibit limitations due to the imaging mechanism. Computerized tomography (CT) images can provide bone tissue information with high resolutions, but they cannot show the soft tissue. Although magnetic resonance imaging (MRI) images are good at showing the anatomical information of soft tissue, they cannot provide metabolism information, while T2 weighted MRI (MRI-T2) and magnetic resonance angiography (MRA) images could achieve good vision of pathological tissue. 2 In practical clinical applications, the multi-modal medical image fusion technology, which combines the complementary information from multi-modal images, is helpful to facilitate a better observation of anatomical and metabolism information. 3 This technology provides an easy but very efficient access for diagnosis and assessment of disease. 4 Image fusion based on multiscale transform has become a more common approach for pixel-level image fusion and abundant research results have been achieved. For waveletbased fusion methods, wavelet transforms are performed first on each source image to obtain different sub-bands which contain decomposition information of varying

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Section: Methodsmentioning

confidence: 99%

Section: Fusion Of Noisy Imagesmentioning

confidence: 99%

Fusion of noisy images based on joint distribution model in dual‐tree complex wavelet domain

Sun

Zhu

Luo

et al. 2018

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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“…Particularly, wavelet-based image fusion algorithms using the Discrete Wavelet Transform (DWT) [9], Stationary Wavelet Transform (SWT) [10], and Dual Tree Complex Wavelet Transform (DT-CWT) [5] have been proposed. The advantage of the DT-CWT relative to the DWT and SWT lies in its added directionality and its balance between overcompleteness and near shift invariance [11].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, image fusion plays a crucial role for computer vision and robotics systems, as fusion results can be used to aid further processing steps for a given task. Therefore, image fusion techniques are practical and essential for many applications, including multi-spectral remote sensing [4] and surveillance for homeland security [5]. For example, infrared images provide information of intruder or potential threat objects, while visible light images provide highresolution structural information.…”

Section: Introductionmentioning

confidence: 99%

Image fusion using the Parameterized Logarithmic Dual Tree Complex Wavelet Transform

Nercessian

Panetta

Agaian

2010

2010 IEEE International Conference on Technologies for Homeland Security (HST)

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Image fusion combines multiple images into a single image containing the relevant information from each of the original source images. This paper introduces a new Parameterized Logarithmic Dual Tree Complex Wavelet Transform (PL-DT-CWT) and its application for image fusion. The new transform combines the Dual Tree Complex Wavelet Transform (DT-CWT) with the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear image processing framework for processing images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means. Keywords-image fusion; parameterized logarithmic image processing; dual tree complex wavelet transform

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“…Visible light images provide high-resolution structural information based on the way in which light is reflected. Thus, the fusion of thermal/infrared and visible images can be used to aid navigation, concealed weapon detection, and surveillance/border patrol by humans or automated computer vision security systems (Qiong et al, 2008). In remote sensing applications, the fusion of multi-spectral low-resolution remote sensing images with a high-resolution panchromatic image can yield a high-resolution multispectral image with good spectral and spatial characteristics (Chibani, 2005).…”

Section: Introductionmentioning

confidence: 99%