Merging Thermal And Visual Images By A Contrast Pyramid

Toet, Alexander; Ruyven, L. J. Van; Valeton, J. M.

doi:10.1117/12.7977034

Cited by 428 publications

(176 citation statements)

References 6 publications

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“…Mainly the quality of fused image depends on the two factors: MSD transform used for decomposition and the fusion rule used to combine the coefficients. Initially Toet, [6] and Toet et al [8] introduced different pyramid schemes for multi sensor image fusion. Pyramid scheme failed to provide any spatial orientation selectivity in the decomposition processes.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Ganasala

Kumar

2014

J Digit Imaging

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“…In contrast to standard methods in remote sensing (e.g. Toet et al, 1989;Ehlers et al, 2009), applying pan-sharpening to non-planar objects in close-range photogrammetry either requires identical perspectives for each image source, or given 3D object models and full orientation parameters of each image. The example above still shows remaining geometric errors at the back parts of the roof since only a 2D rectification approach has been used.…”

Section: Pan Sharpeningmentioning

confidence: 99%

Geometric Calibration of Thermographic Cameras

Lühmann

Piechel

Roelfs

2013

Remote Sensing and Digital Image Processing

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Commission V, WG V/5KEY WORDS: Thermography, Thermal Imaging, Calibration, Accuracy, Application ABSTRACT:The paper gives an overview of thermal imaging sensors for photogrammetric close-range applications. In particular, it presents results of the geometric calibration of thermographic cameras as they are used for building inspection and material testing. Two different test fields have been designed providing point targets that are visible in the thermal spectral band of the cameras.Five different cameras have been investigated. Four of them have solid state sensors with pixel sizes between 25 and 40 µm. One camera is working in scanning mode. The lenses for thermographic cameras are made of Germanium. Conventional imaging configurations (typically 20 images) have been used for camera calibration. Standard parameters for principal distance, principal point, radial distortion, decentring distortion, affinity and shear have been introduced into the self-calibrating bundle adjustment. All measured points are introduced as weighted control points. Image coordinates have been measured either in the professional software package AICON 3D Studio (ellipse operators), or in the IAPG software system Stereomess (least-squares template matching).The calibration results differ significantly from camera to camera. All lenses show relatively large decentring distortion and deviations from orthogonality of the image coordinate axes. Using the plane test field with lamps, the average image precision is 3/10 th of a pixel while the 3D test field with circular reflecting targets results in imaging errors of 1/20 th pixel.

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“…The Laplacian Pyramid [6] implements a -pattern selective approach to image fusion, so that the composite image is constructed not a pixel at a time, but a feature at a time. The basic idea is to perform a pyramid decomposition on each source image, then integrate all these decompositions to form a composite representation, and finally reconstruct the fused image by performing an inverse pyramid transform [16].…”

Section: Laplacian Pyramid Based Image Fusionmentioning

confidence: 99%

Multiresolution SVD based Image Fusion

Kumar¹

2017

IOSR JVSP

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Merging Thermal And Visual Images By A Contrast Pyramid

Cited by 428 publications

References 6 publications

CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain

Geometric Calibration of Thermographic Cameras

Multiresolution SVD based Image Fusion

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