A novel similarity based quality metric for image fusion

Yang, Chao; Zhang, Jianqi; Wang, Xiaorui; Liu, Xin

doi:10.1016/j.inffus.2006.09.001

Cited by 399 publications

(144 citation statements)

References 9 publications

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“…These metrics include edge retention (Q AB/F ) [53,54], mutual information (MI) [55], visual information fidelity (VIF) [56], Yang proposed fusion metric (Q Y ) [57,58], and Chen-Blum metric (Q CB ) [58,59]. For the fused image, the sizes of Q AB/F , MI, VIF, Q Y , and Q CB become bigger, the corresponding fusion results are better.…”

Section: Objective Evaluation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Zhu

Chai

et al. 2017

Applied Sciences

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Abstract:In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA) to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP) algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution.

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Section: Objective Evaluation Methodsmentioning

confidence: 99%

“…Yang et al proposed structural similarity-Based way for fusion assessment [57]. The Yang's method is shown in Equation (11).…”

Section: Q Ymentioning

confidence: 99%

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Zhu

Chai

et al. 2017

Applied Sciences

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“…The dynamic range for Q f is [0,1] and it should be as close to 1 as possible for better fusion. See [8] for the detailed implementation of the aforementioned metric.…”

Section: Fig 5: Multimodal Medical Images Using Different Data Setsmentioning

confidence: 99%

A Novel Multimodal Medical Image Fusion Approach based on Phase Congruency and Directive Contrast in NSCT Domain

Pillai¹,

Subhashini²,

Kamaraju³

2015

IJCA

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In this paper, Non-subsampled medical image fusion is a unique tool, which develops many imaging techniques in medical field. The main work is to capture the information from different image sources and convert them into single output. Two different fusion rules like phase congruency and directive contrast are introduced. The work was discussed based on the images in medical field which gives accurate results with less distortion is based upon transformation and parameters. The main drawback of previous methods are they cannot produce a color image for better clarity and accurate analysis of medical image. In this paper, the parameters such as Mutual Information(MI), Edge Based Similarity Measure(QAB/F), Structural Information Metric(Qe), Degree Of Distortion(Qo) and Normalization Of Image(Qw) are introduced to increase the visual perception of an image. The parameters and lab-transform is used for better quality of the image. KeywordsNSCT domain, MRI image, CT image, phase congruency and directive contrast.

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“…In recent years, a number of computational image fusion quality assessment metrics have therefore been proposed (e.g. Angell, 2005;Blum, 2006;Chari et al, 2005;Chen & Varshney, 2005;Chen & Varshney, 2007;Corsini et al, 2006;Cvejic et al, 2005a;Cvejic et al, 2005b;Piella & Heijmans, 2003;Toet & Hogervorst, 2003;Tsagiris & Anastassopoulos, 2004;Ulug & Claire, 2000;Wang & Shen, 2006;Xydeas & Petrovic, 2000;Yang et al, 2007;Zheng et al, 2007;Zhu & Jia, 2005). Although some of these metrics agree with human visual perception to some extent, most of them cannot predict observer performance for different input imagery and scenarios.…”

Section: The Need For Image Fusion Quality Metricsmentioning

confidence: 99%

Cognitive Image Fusion and Assessment

Toet¹

2011

Image Fusion

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A novel similarity based quality metric for image fusion

Cited by 399 publications

References 9 publications

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

A Novel Multimodal Medical Image Fusion Approach based on Phase Congruency and Directive Contrast in NSCT Domain

Cognitive Image Fusion and Assessment

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