Redundant versus orthogonal wavelet decomposition for multisensor image fusion

Chibani, Youcef; Houacine, Amrane

doi:10.1016/s0031-3203(02)00103-6

Cited by 108 publications

(71 citation statements)

References 15 publications

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“…4 and in addition, some soft tissue present in the CT scan is not seen in the MRI image provided. In contrast, the MRI image [7] uses image brightness to indicate the amount of hydrogen atoms present in the soft tissue thus, the brightness of soft tissue is higher and bones can no longer been seen. There is complementary information present within these images however and to illustrate this we use three methods of fusion that have already been discussed in context of medical imaging and adopt the same fusion standards used in our IDFCT method.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

Sumathi

Hemalatha

2011

Open Computer Science

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Image fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.Keywords: region based energy method • discrete wavelet transform • discrete fast curvelet transform • root mean square error © Versita sp. z o.o.

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Section: Experimental Results and Analysismentioning

confidence: 99%

An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

Sumathi

Hemalatha

2011

Open Computer Science

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“…A similar problem of deconvolution is faced in spectroscopy, where the entire emission spectrum is collected (42)(43)(44)(45). Consequently, several mathematical deconvolution techniques have been developed to separate backgrounds and discrete spectral components (46)(47)(48). Some algorithms have even shown success at separating signal components with little or no a priori information about the sample (49,50).…”

Section: Discussionmentioning

confidence: 99%

Single particle high resolution spectral analysis flow cytometry

et al. 2006

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Background: While conventional multiparameter flow cytometers have proven highly successful, there are several types of analytical measurements that would benefit from a more comprehensive and flexible approach to spectral analysis including, but certainly not limited to spectral deconvolution of overlapping emission spectra, fluorescence resonance energy transfer measurements, metachromic dye analysis, free versus bound dye resolution, and Raman spectroscopy. Methods: Our system utilizes a diffraction grating to disperse the collected fluorescence and side-scattered light from cells or microspheres passing through the interrogation region over a rectangular charge-coupled-device image sensor. The flow cell and collection optics are taken from a conventional flow cytometer with minimal modifications to assure modularity of the system. Results: Calibration of the prototype spectral analysis flow cytometer included wavelength characterization and calibration of the dispersive optics. Benchmarking of the system demonstrated a single particle/cell intensity sensitivity of 2160 MESF of R-Phycoerythrin. Single particle spectra taken with our instrument were validated against bulk solution fluorimeter and conventional flow cytometer measurements. Coefficients of variation of integrated

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“…There are many methods discovered and discussed in literature that focus on image fusion. They vary with the aim of application used, but they can be mainly categorized due to algorithms used into pyramid techniques [10,11], morphological methods [3,4,5], discrete wavelet transform [12,13,14] and neural network fusion [15]. The different classification of image fusion involves pixel, feature and symbolic levels [16].…”

Section: Overviewmentioning

confidence: 99%

Modeling of 3D Scene Based on Series of Photographs Taken with Different Depth-of-Field

Denkowski

Chlebiej

Mikołajczak

2008

Computational Science – ICCS 2008

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Abstract. This paper presents a method for fusing multifocus images into enhanced depth-of-field composite image and creating a 3D model of a photographed scene. A set of images of the same scene is taken from a typical digital camera with macro lenses with different depth-of-field. The method employs convolution and morphological filters to designate sharp regions in this set of images and combine them together into an image where all regions are properly focused. The presented method consists of several phases including: image registration, height map creation, image reconstruction and final 3D scene reconstruction. In result a 3D model of the photographed object is created.

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Redundant versus orthogonal wavelet decomposition for multisensor image fusion

Cited by 108 publications

References 15 publications

An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

Single particle high resolution spectral analysis flow cytometry

Modeling of 3D Scene Based on Series of Photographs Taken with Different Depth-of-Field

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