Maximum local energy: An effective approach for multisensor image fusion in beyond wavelet transform domain

Lu, Huimin; Zhang, Lifeng; Serikawa, Seiichi

doi:10.1016/j.camwa.2012.03.017

Cited by 81 publications

(36 citation statements)

References 20 publications

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“…Further, proposed method is compared with two other methods, (1) energy-and contrast-based fusion rule in Contourlet Transform domain (CT-Energy-contrast) [19] and (II) energy-and SML-based fusion rule in Contourlet Transform domain (CT-Energy-SML) [22] by using Dataset-1. Comparison with CT-Energy-contrast method is done with respect to entropy (EN), overall cross entropy (OCE), spatial frequency (SF), and cross correlation coefficient (CC(A,F), CC(B,F)) quality metrics as followed in [19].…”

Section: Mi: Mutual Information Is the Amount Of Information Thatmentioning

confidence: 99%

“…Hence, the performance of the proposed method is better than CT-Energy-contrast method. Universal Image Quality Index-based metrics are used for comparison of proposed method with CT-Energy-SML method as followed in [22]. These results are shown in Table 4.…”

Section: Mi: Mutual Information Is the Amount Of Information Thatmentioning

confidence: 99%

“…Shift-invariant version of Contourlet transform called nonsubsampled contourlet transform (NSCT) which is proposed by da Cunha et al [20] has been used for image fusion in different applications. NSCT gives better performance for medical image fusion due to flexible multiscale, multidirection, and shift-invariant image decomposition [21,22]. In the proposed method, NSCT is used for multiscale decomposition of source images.…”

Section: Introductionmentioning

confidence: 99%

“…For low-frequency coefficient fusion, features like energy [22], visibility [25], weighted energy, entropy [26], and spatial frequency [27], etc. are used as activity measurements by researchers.…”

Section: Introductionmentioning

confidence: 99%

“…are used as activity measurements by researchers. For highfrequency coefficient fusion features like contrast [28], gradient, variance [25], sum-modified laplacian (SML) [22], energy of gradient, and energy of laplacian, etc. are used as activity level measurement parameters by researchers.…”

Section: Introductionmentioning

confidence: 99%

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CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Ganasala

Kumar

2014

J Digit Imaging

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Fusion of CT and MR images allows simultaneous visualization of details of bony anatomy provided by CT image and details of soft tissue anatomy provided by MR image. This helps the radiologist for the precise diagnosis of disease and for more effective interventional treatment procedures. This paper aims at designing an effective CT and MR image fusion method. In the proposed method, first source images are decomposed by using nonsubsampled contourlet transform (NSCT) which is a shift-invariant, multiresolution and multidirection image decomposition transform. Maximum entropy of square of the coefficients with in a local window is used for low-frequency sub-band coefficient selection. Maximum weighted sum-modified Laplacian is used for high-frequency sub-bands coefficient selection. Finally fused image is obtained through inverse NSCT. CT and MR images of different cases have been used to test the proposed method and results are compared with those of the other conventional image fusion methods. Both visual analysis and quantitative evaluation of experimental results shows the superiority of proposed method as compared to other methods.

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Section: Mi: Mutual Information Is the Amount Of Information Thatmentioning

confidence: 99%

Section: Mi: Mutual Information Is the Amount Of Information Thatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Ganasala

Kumar

2014

J Digit Imaging

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Identification of tropical cyclone centre based on satellite images via deep learning techniques

Long

Tong

et al. 2022

Intl Journal of Climatology

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A tropical cyclone (TC) is a highly destructive natural disaster. Accurate identification of key parameters of TCs is prerequisite for most TC-related research and practices. The centre position is one of TC's basic parameters. However, comparison of TC best track data released by different meteorological institutes usually indicates a noticeable discrepancy for this parameter among varied data sources. In this study, efforts are made towards identifying the centre location of TCs via deep learning techniques, based on TC satellite cloud images (SCIs). Six deep learning models are analysed and compared. YOLOv4 model achieved a confidence of 99.84%, which is better than other models. In addition, we further explore the factors affecting the positioning accuracy of the YOLOv4 model and its application to the location identification of multiple TCs and the tracking of individual TCs. Results demonstrate that the YOLOv4 model has a probability exceeding 99% for identifying multiple TC locations and also performs well for single TC tracking.

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DeepEye: A Dedicated Camera for Deep-Sea Tripod Observation Systems

Kim

et al. 2019

Cognitive Internet of Things: Frameworks, Tools and Applications

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Maximum local energy: An effective approach for multisensor image fusion in beyond wavelet transform domain

Cited by 81 publications

References 20 publications

CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Identification of tropical cyclone centre based on satellite images via deep learning techniques

DeepEye: A Dedicated Camera for Deep-Sea Tripod Observation Systems

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