This paper addresses four different aspects of the remote sensing image fusion: i) image fusion method, ii)
quality analysis of fusion results, iii) effects of image decomposition level, and iv) importance of image
registration. First, a new contourlet-based image fusion method is presented, which is an improvement
over the wavelet-based fusion. This fusion method is then utilized withinthe main fusion process to analyze
the final fusion results. Fusion framework, scheme and datasets used in the study are discussed in detail.
Second, quality analysis of the fusion results is discussed using various quantitative metrics for both spatial
and spectral analyses. Our results indicate that the proposed contourlet-based fusion method performs
better than the conventional wavelet-based fusion methodsin terms of both spatial and spectral analyses.
Third, we conducted an analysis on the effects of the image decomposition level and observed that the
decomposition level of 3 produced better fusion results than both smaller and greater number of levels.
Last, we created four different fusion scenarios to examine the importance of the image registration. As a
result, the feature-based image registration using the edge features of the source images produced better
fusion results than the intensity-based imageregistration.
Performance analysis of contourlet based hyperspectral image fusion methodijitjournal
Recently, contourlet transform has been widely used in hyperspectral image fusion due to its advantages,
such as high directionality and anisotropy; and studies show that the contourlet-based fusion methods
perform better than the existing conventional methods including wavelet-based fusion methods. Few studies
have been done to comparatively analyze the performance of contourlet-based fusion methods;
furthermore, no research has been done to analyze the contourlet-based fusion methods by focusing on
their unique transform mechanisms. In addition, no research has focused on the original contourlet
transform and its upgraded versions. In this paper, we investigate three different kinds of contourlet
transform: i) original contourlet transform, ii) nonsubsampled contourlet transform, iii) contourlet
transform with sharp frequency localization. The latter two transforms were developed to overcome the
major drawbacks of the original contourlet transform; so it is necessary and beneficial to see how they
perform in the context of hyperspectral image fusion. The results of our comparative analysis show that the
latter two transforms perform better than the original contourlet transform in terms of increasing spatial
resolution and preserving spectral information.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
A novel approach to Image Fusion using combination of Wavelet Transform and C...IJSRD
Panchromatic furthermore multi-spectral image fusion outstands common methods of high-resolution color image amalgamation. In digital image reconstruction, image fusion is standout pre-processing step that aims increasing hotspot image quality to extricate all suitable information from source images ruining inconsistencies or artifacts. Around the different strategies available for image fusion, Wavelet and Curvelet based algorithms are mostly preferred. Wavelet transform is useful for point singularities while Curvelet transform, as the name describes, is more useful for the analysis of images having curved shape edges. This paper reveals a study of development in the field of image fusion.
Image Registration using NSCT and Invariant MomentCSCJournals
Image registration is a process of matching images, which are taken at different times, from different sensors or from different view points. It is an important step for a great variety of applications such as computer vision, stereo navigation, medical image analysis, pattern recognition and watermarking applications. In this paper an improved feature point selection and matching technique for image registration is proposed. This technique is based on the ability of nonsubsampled contourlet transform (NSCT) to extract significant features irrespective of feature orientation. Then the correspondence between the extracted feature points of reference image and sensed image is achieved using Zernike moments. Feature point pairs are used for estimating the transformation parameters mapping the sensed image to the reference image. Experimental results illustrate the registration accuracy over a wide range for panning and zooming movement and also the robustness of the proposed algorithm to noise. Apart from image registration proposed method can be used for shape matching and object classification. Keywords: Image Registration, NSCT, Contourlet Transform, Zernike Moment.
Geometric wavelet transform for optical flow estimation algorithmijcga
This paper described an algorithm for computing the optical flow (OF) vector of a moving objet in a video sequence based on geometric wavelet transform (GWT). This method tries to calculate the motion between two successive frames by using a GWT. It consists to project the OF vectors on a basis of geometric wavelet. Using GWT for OF estimation has been attracting much attention. This approach takes advantage of the geometric wavelet filter property and requires only two frames. This algorithm is fast and able to estimate the OF with a low-complexity. The technique is suitable for video compression, and can be used for stereo vision and image registration.
A Comparative Study of Wavelet and Curvelet Transform for Image DenoisingIOSR Journals
Abstract : This paper describes a comparison of the discriminating power of the various multiresolution based thresholding techniques i.e., Wavelet, curve let for image denoising.Curvelet transform offer exact reconstruction, stability against perturbation, ease of implementation and low computational complexity. We propose to employ curve let for facial feature extraction and perform a thorough comparison against wavelet transform; especially, the orientation of curve let is analysed. Experiments show that for expression changes, the small scale coefficients of curve let transform are robust, though the large scale coefficients of both transform are likely influenced. The reason behind the advantages of curvelet lies in its abilities of sparse representation that are critical for compression, estimation of images which are denoised and its inverse problems, thus the experiments and theoretical analysis coincide . Keywords: Curvelet transform, Face recognition, Feature extraction, Sparse representation Thresholding rules,Wavelet transform..
Performance analysis of contourlet based hyperspectral image fusion methodijitjournal
Recently, contourlet transform has been widely used in hyperspectral image fusion due to its advantages,
such as high directionality and anisotropy; and studies show that the contourlet-based fusion methods
perform better than the existing conventional methods including wavelet-based fusion methods. Few studies
have been done to comparatively analyze the performance of contourlet-based fusion methods;
furthermore, no research has been done to analyze the contourlet-based fusion methods by focusing on
their unique transform mechanisms. In addition, no research has focused on the original contourlet
transform and its upgraded versions. In this paper, we investigate three different kinds of contourlet
transform: i) original contourlet transform, ii) nonsubsampled contourlet transform, iii) contourlet
transform with sharp frequency localization. The latter two transforms were developed to overcome the
major drawbacks of the original contourlet transform; so it is necessary and beneficial to see how they
perform in the context of hyperspectral image fusion. The results of our comparative analysis show that the
latter two transforms perform better than the original contourlet transform in terms of increasing spatial
resolution and preserving spectral information.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
A novel approach to Image Fusion using combination of Wavelet Transform and C...IJSRD
Panchromatic furthermore multi-spectral image fusion outstands common methods of high-resolution color image amalgamation. In digital image reconstruction, image fusion is standout pre-processing step that aims increasing hotspot image quality to extricate all suitable information from source images ruining inconsistencies or artifacts. Around the different strategies available for image fusion, Wavelet and Curvelet based algorithms are mostly preferred. Wavelet transform is useful for point singularities while Curvelet transform, as the name describes, is more useful for the analysis of images having curved shape edges. This paper reveals a study of development in the field of image fusion.
Image Registration using NSCT and Invariant MomentCSCJournals
Image registration is a process of matching images, which are taken at different times, from different sensors or from different view points. It is an important step for a great variety of applications such as computer vision, stereo navigation, medical image analysis, pattern recognition and watermarking applications. In this paper an improved feature point selection and matching technique for image registration is proposed. This technique is based on the ability of nonsubsampled contourlet transform (NSCT) to extract significant features irrespective of feature orientation. Then the correspondence between the extracted feature points of reference image and sensed image is achieved using Zernike moments. Feature point pairs are used for estimating the transformation parameters mapping the sensed image to the reference image. Experimental results illustrate the registration accuracy over a wide range for panning and zooming movement and also the robustness of the proposed algorithm to noise. Apart from image registration proposed method can be used for shape matching and object classification. Keywords: Image Registration, NSCT, Contourlet Transform, Zernike Moment.
Geometric wavelet transform for optical flow estimation algorithmijcga
This paper described an algorithm for computing the optical flow (OF) vector of a moving objet in a video sequence based on geometric wavelet transform (GWT). This method tries to calculate the motion between two successive frames by using a GWT. It consists to project the OF vectors on a basis of geometric wavelet. Using GWT for OF estimation has been attracting much attention. This approach takes advantage of the geometric wavelet filter property and requires only two frames. This algorithm is fast and able to estimate the OF with a low-complexity. The technique is suitable for video compression, and can be used for stereo vision and image registration.
A Comparative Study of Wavelet and Curvelet Transform for Image DenoisingIOSR Journals
Abstract : This paper describes a comparison of the discriminating power of the various multiresolution based thresholding techniques i.e., Wavelet, curve let for image denoising.Curvelet transform offer exact reconstruction, stability against perturbation, ease of implementation and low computational complexity. We propose to employ curve let for facial feature extraction and perform a thorough comparison against wavelet transform; especially, the orientation of curve let is analysed. Experiments show that for expression changes, the small scale coefficients of curve let transform are robust, though the large scale coefficients of both transform are likely influenced. The reason behind the advantages of curvelet lies in its abilities of sparse representation that are critical for compression, estimation of images which are denoised and its inverse problems, thus the experiments and theoretical analysis coincide . Keywords: Curvelet transform, Face recognition, Feature extraction, Sparse representation Thresholding rules,Wavelet transform..
Image fusion is an image enhancement approach for increasing the visual perception from two or more image into a single image. Each image is obtained from different object in focus. This process is now broadly used in various application of image processing such as medical imaging such as MRI, CT [18] and PET, remote sensing, satellite imaging, in design of intelligent robot etc. In this paper we have gone through the literature work done by the various researchers to obtain high quality improved image by combining important and desirable features from two or more images into a single image. Different image fusion rules, like maximum selection scheme, weighted average scheme and window based verification scheme are discussed. We also discuss about the image fusion techniques using DWT. Distinct blurred images are fused using DWT, SWT and using local correlation and their results are compared. The role of fusion in image enhancement is also taken into consideration. The results of different fusion technique are also compared, which are furnished in pictures and tables.
Contourlet Transform Based Method For Medical Image DenoisingCSCJournals
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Performance analysis of Hybrid Transform, Hybrid Wavelet and Multi-Resolutio...IJMER
Compression of digital images play vital role in transmission of multimedia data. This paper
presents application of hybrid wavelet transform in image compression. Multi-resolution property of
Wavelet transform helps to analyze the information contents of image effectively. This property has been
used in image compression application. Hybrid wavelet transform is generated using two different
component transforms. Various sizes of these component transforms can be used. In this hybrid wavelet,
global and local properties of component transforms are incorporated and hence are called bi-resolution
analysis. Different levels of resolutions can also be included in generated hybrid transform. Hence It is
called multi-resolution analysis and is applied on images. At each level of resolution number of
components can be changed. It provides great flexibility to generate hybrid transform matrix. Image is
compressed using hybrid wavelet, hybrid wavelet with multi-resolution and hybrid transform. Their
performance is compared and it has been observed that hybrid wavelet transform gives lower error
values than multi-resolution analysis and hybrid transform. Along with Root mean Square Error (RMSE),
Mean Absolute Error (MAE) and Average Fractional Change in Pixel Value (AFCPV) is used to measure
error. AFCPV gives better perception to image quality as it is a fractional change in pixel values. Lower
the value of AFCPV better is the image quality.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...csandit
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
EFFICIENT IMAGE COMPRESSION USING LAPLACIAN PYRAMIDAL FILTERS FOR EDGE IMAGESijcnac
This project presents a new image compression technique for the coding of retinal and
fingerprint images. Retinal images are used to detect diseases like diabetes or
hypertension. Fingerprint images are used for the security purpose. In this work, the
contourlet transform of the retinal and fingerprint image is taken first. The coefficients of
the contourlet transform are quantized using adaptive multistage vector quantization
scheme. The number of code vectors in the adaptive vector quantization scheme depends
on the dynamic range of the input image.
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...cscpconf
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
Image fusion is an image enhancement approach for increasing the visual perception from two or more image into a single image. Each image is obtained from different object in focus. This process is now broadly used in various application of image processing such as medical imaging such as MRI, CT [18] and PET, remote sensing, satellite imaging, in design of intelligent robot etc. In this paper we have gone through the literature work done by the various researchers to obtain high quality improved image by combining important and desirable features from two or more images into a single image. Different image fusion rules, like maximum selection scheme, weighted average scheme and window based verification scheme are discussed. We also discuss about the image fusion techniques using DWT. Distinct blurred images are fused using DWT, SWT and using local correlation and their results are compared. The role of fusion in image enhancement is also taken into consideration. The results of different fusion technique are also compared, which are furnished in pictures and tables.
Contourlet Transform Based Method For Medical Image DenoisingCSCJournals
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Performance analysis of Hybrid Transform, Hybrid Wavelet and Multi-Resolutio...IJMER
Compression of digital images play vital role in transmission of multimedia data. This paper
presents application of hybrid wavelet transform in image compression. Multi-resolution property of
Wavelet transform helps to analyze the information contents of image effectively. This property has been
used in image compression application. Hybrid wavelet transform is generated using two different
component transforms. Various sizes of these component transforms can be used. In this hybrid wavelet,
global and local properties of component transforms are incorporated and hence are called bi-resolution
analysis. Different levels of resolutions can also be included in generated hybrid transform. Hence It is
called multi-resolution analysis and is applied on images. At each level of resolution number of
components can be changed. It provides great flexibility to generate hybrid transform matrix. Image is
compressed using hybrid wavelet, hybrid wavelet with multi-resolution and hybrid transform. Their
performance is compared and it has been observed that hybrid wavelet transform gives lower error
values than multi-resolution analysis and hybrid transform. Along with Root mean Square Error (RMSE),
Mean Absolute Error (MAE) and Average Fractional Change in Pixel Value (AFCPV) is used to measure
error. AFCPV gives better perception to image quality as it is a fractional change in pixel values. Lower
the value of AFCPV better is the image quality.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
A New Approach for Segmentation of Fused Images using Cluster based ThresholdingIDES Editor
This paper proposes the new segmentation technique
with cluster based method. In this, the multi source medical
images like MRI (Magnetic Resonance Imaging), CT
(computed tomography) & PET (positron emission
tomography) are fused and then segmented using cluster based
thresholding approach. The edge details of an image have
become an essential technique in clinical and researchoriented
applications. The more edge details of the fused image
have obtainable with this method. The objective of the
clustering process is to partition a fused image coefficients
into a number of clusters having similar features. These
features are useful to generate the threshold value for further
segmentation of fused image. Finally the segmented output
is compared with standard FCM method and modified Otsu
method. Experimental results have shown that the proposed
cluster based thresholding method is able to effectively extract
important edge details of fused image.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...csandit
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
EFFICIENT IMAGE COMPRESSION USING LAPLACIAN PYRAMIDAL FILTERS FOR EDGE IMAGESijcnac
This project presents a new image compression technique for the coding of retinal and
fingerprint images. Retinal images are used to detect diseases like diabetes or
hypertension. Fingerprint images are used for the security purpose. In this work, the
contourlet transform of the retinal and fingerprint image is taken first. The coefficients of
the contourlet transform are quantized using adaptive multistage vector quantization
scheme. The number of code vectors in the adaptive vector quantization scheme depends
on the dynamic range of the input image.
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...cscpconf
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Remote Sensing Image Fusion Using Contourlet Transform With Sharp Frequency Localization
1. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
DOI : 10.5121/ijitmc.2014.2103 23
REMOTE SENSING IMAGE FUSION USING CONTOURLET
TRANSFORM WITH SHARP FREQUENCY LOCALIZATION
Yoonsuk Choi*, Ershad Sharifahmadian, Shahram Latifi
Dept. of Electrical and Computer Engineering, University of Nevada, Las Vegas
4505 Maryland Parkway, Las Vegas, NV 89154-4026
ABSTRACT
This paper addresses four different aspects of the remote sensing image fusion: i) image fusion method, ii)
quality analysis of fusion results, iii) effects of image decomposition level, and iv) importance of image
registration. First, a new contourlet-based image fusion method is presented, which is an improvement
over the wavelet-based fusion. This fusion method is then utilized withinthe main fusion process to analyze
the final fusion results. Fusion framework, scheme and datasets used in the study are discussed in detail.
Second, quality analysis of the fusion results is discussed using various quantitative metrics for both spatial
and spectral analyses. Our results indicate that the proposed contourlet-based fusion method performs
better than the conventional wavelet-based fusion methodsin terms of both spatial and spectral analyses.
Third, we conducted an analysis on the effects of the image decomposition level and observed that the
decomposition level of 3 produced better fusion results than both smaller and greater number of levels.
Last, we created four different fusion scenarios to examine the importance of the image registration. As a
result, the feature-based image registration using the edge features of the source images produced better
fusion results than the intensity-based imageregistration.
KEYWORDS
Contourlet, Hyperspectral, Multispectral, Panchromatic, Wavelet
1. INTRODUCTION
Remote sensing technologies provide remotely observed images with high spatial and spectral
resolutions. However, due to the hardware limitations, current remote sensors provide spectral
images that have low spatial resolution; hence, image fusion becomes necessary in order to fuse
spectral images with high-resolution spatial images. The resultant fusion image provides
scientists with more useful information both spatially and spectrally [1].
Recently, a new transform technique namely wavelet transform (WT) has been widely used in the
field of image fusion due to its multi-resolution decomposition where input images are
decomposed into different scales or levels of frequencies. However, the wavelet-based fusion
techniques are good in detecting discontinuities at edge points but not optimal in capturing two-
dimensional singularities from the input images. Moreover, thesewavelets exhibit limited
capabilities in detecting the smoothness along the contours of the input images [2]. Do and
Vetterli introduced a new two-dimensional contourlet transform (CT) [3]. This transform is
capable of constructing both multi-resolution and multi-directional expansions using a non-
separable pyramid directional filter bank (PDFB) [4].
2. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
24
Despite the advantages, Do’s group has enhanced the original contourlet transform theory by
developingnew contourlet-based transforms in order to overcome some drawbacks of the original
contourlet transform. The first one is the nonsubsampled contourlet transform (NSCT) [5] and the
second one is the contourlet transform with sharp frequency localization (CTSFL) [6].
In this paper, we mainly focus on the contourlet transform with sharp frequency localization due
to its advantages in image processing, and present a new remote sensing image fusion method
based on CTSFL. In Section 2 and Section 3, we discuss the principle, advantages and drawbacks
of CT and CTSFL respectively. In Section 4, we present a new image fusion method based on
CTSFL including the fusion framework and fusion scheme in detail. Then, we discuss the
experiments and analyze the fusion results in Section 5, and conclusion is provided in Section 6.
2. CONTOUR LET TRANSFORM
The contourlet transform is a multi-scale and multi-direction framework of discrete image. The
multi-scale and multi-directional analyses are separated in series. First, a Laplacian pyramid (LP)
[7] is used to capture point discontinuities in input images.Second, a directional filter bank (DFB)
[8] is applied to form linear structures by linkingthe point discontinuities. As a result,it
becomespossible to obtain an image expansion using basic elements like contour segments.
Thecontourlet transform framework is shown in Figure 1.
Figure 1. The contourlet transform framework
Figure 2 shows the contourlet filter bank. First, multi-scale decomposition is performed by the
Laplacian pyramid, and then a directional filter bank is applied to each band pass channel.
Figure 2. The contourlet filter bank
The contourlet expansion of images consists of basis images oriented at various directions in
multiple scales with flexible aspect ratio. In addition to retaining the multi-scale and time-
International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
24
Despite the advantages, Do’s group has enhanced the original contourlet transform theory by
developingnew contourlet-based transforms in order to overcome some drawbacks of the original
contourlet transform. The first one is the nonsubsampled contourlet transform (NSCT) [5] and the
second one is the contourlet transform with sharp frequency localization (CTSFL) [6].
In this paper, we mainly focus on the contourlet transform with sharp frequency localization due
to its advantages in image processing, and present a new remote sensing image fusion method
based on CTSFL. In Section 2 and Section 3, we discuss the principle, advantages and drawbacks
of CT and CTSFL respectively. In Section 4, we present a new image fusion method based on
CTSFL including the fusion framework and fusion scheme in detail. Then, we discuss the
experiments and analyze the fusion results in Section 5, and conclusion is provided in Section 6.
2. CONTOUR LET TRANSFORM
The contourlet transform is a multi-scale and multi-direction framework of discrete image. The
multi-scale and multi-directional analyses are separated in series. First, a Laplacian pyramid (LP)
[7] is used to capture point discontinuities in input images.Second, a directional filter bank (DFB)
[8] is applied to form linear structures by linkingthe point discontinuities. As a result,it
becomespossible to obtain an image expansion using basic elements like contour segments.
Thecontourlet transform framework is shown in Figure 1.
Figure 1. The contourlet transform framework
Figure 2 shows the contourlet filter bank. First, multi-scale decomposition is performed by the
Laplacian pyramid, and then a directional filter bank is applied to each band pass channel.
Figure 2. The contourlet filter bank
The contourlet expansion of images consists of basis images oriented at various directions in
multiple scales with flexible aspect ratio. In addition to retaining the multi-scale and time-
International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
24
Despite the advantages, Do’s group has enhanced the original contourlet transform theory by
developingnew contourlet-based transforms in order to overcome some drawbacks of the original
contourlet transform. The first one is the nonsubsampled contourlet transform (NSCT) [5] and the
second one is the contourlet transform with sharp frequency localization (CTSFL) [6].
In this paper, we mainly focus on the contourlet transform with sharp frequency localization due
to its advantages in image processing, and present a new remote sensing image fusion method
based on CTSFL. In Section 2 and Section 3, we discuss the principle, advantages and drawbacks
of CT and CTSFL respectively. In Section 4, we present a new image fusion method based on
CTSFL including the fusion framework and fusion scheme in detail. Then, we discuss the
experiments and analyze the fusion results in Section 5, and conclusion is provided in Section 6.
2. CONTOUR LET TRANSFORM
The contourlet transform is a multi-scale and multi-direction framework of discrete image. The
multi-scale and multi-directional analyses are separated in series. First, a Laplacian pyramid (LP)
[7] is used to capture point discontinuities in input images.Second, a directional filter bank (DFB)
[8] is applied to form linear structures by linkingthe point discontinuities. As a result,it
becomespossible to obtain an image expansion using basic elements like contour segments.
Thecontourlet transform framework is shown in Figure 1.
Figure 1. The contourlet transform framework
Figure 2 shows the contourlet filter bank. First, multi-scale decomposition is performed by the
Laplacian pyramid, and then a directional filter bank is applied to each band pass channel.
Figure 2. The contourlet filter bank
The contourlet expansion of images consists of basis images oriented at various directions in
multiple scales with flexible aspect ratio. In addition to retaining the multi-scale and time-
3. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
25
frequency localization properties of wavelets, the contourlet transform offers high degree of
directionality. The contourlet transform adopts nonseparable basis functions, which makes it
capable of capturing the geometrical smoothness of the contour along any possible direction.
Compared to the traditional image expansions, the contourlets can capture 2-D geometrical
structure in natural images much more efficiently [9].
Figure 3 shows a few wavelet and contourlet basis images. We can observe that the contourlets
offer a much richer set of directions and shapes; hence, they are more suitable in capturing
smooth contours and geometric structures in images.
Figure 3. Comparison between actual 2-D wavelets (left) and contourlets (right) [3]
3. CONTOURLET TRANSFORM WITH SHARP FREQUENCY LOCALIZATION
The major drawback of the original contourlet construction is that its basis images are not
localized in the frequency domain. The contourlet transform is constructed as a combination of
the Laplacian pyramid [10] and the directional filter banks (DFB) [8]. Conceptually, the flow of
operation can be illustrated by Figure 4(a), where the Laplacian pyramid iteratively decomposes a
2-D image into lowpass and highpass sub-bands, and the DFB are applied to the highpass sub-
bands to further decompose the frequency spectrum. Using ideal filters, the contourlet transform
will decompose the 2-D frequency spectrum into trapezoid-shaped regions as shown in Figure
4(b).
Figure 4.The original contourlet transform. (a) Block diagram. (b) Resulting frequency division
When non-ideal filters are used, the resulting contourlets do not have the desired sharp frequency
domain localization. Although the majority of the energy in each subband isconcentrated on the
ideal support regions, there are also significant amount of aliasing components at locations far
away from the desired support. This type of frequency aliasing is undesirable because the
4. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
26
resulting contourlets in the spatial domain are not smooth along their main ridges and exhibit
some fuzzy artifacts. As a result, the efficiency is degraded in representing smooth boundaries in
natural images [11]. This phenomenon has also beenobserved by other researches, such as the
study by Candes et al. [12].
Y. Lu and M. N. Do proposed a new method of constructing the contourlet transform to overcome
the drawback [11]. Figure 5 is a new construction of the contourlet transform. Directional filter
bank is still used for directional decomposition; however, a significant difference is that a new
pyramid structure for the multi-scale decomposition is used instead of using the Laplacian
pyramid [13]. As a result, this new multi-scale pyramid can employ a different set of lowpass and
highpass filters for the first level and all other levels [11].
Figure 5.The new block diagram. Only the analysis part is shown, while the synthesis part is exactly
symmetric [11]
4. IMAGE FUSION
4.1.Fusion Framework
The fusion framework used in our experiments is depicted in Figure 6. The general idea is
identical to the image fusion using the original contourlet transform. The source images are
decomposed using contourlet transform and the components are fused together according to a
certain fusion scheme. Then, the final fusion result is obtained by performing inverse contourlet
transform. However, the main difference is the multi-scale decomposition. In other words, the
directional filter bank is still used for directional decomposition, but a new pyramid structure for
the multi-scale decomposition is used instead of using the Laplacian pyramid. This new multi-
scale pyramid can utilize a different set of lowpass and highpass filters for the first level and all
other levels.
Figure 6.The fusion framework based on CTSFL
5. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
27
4.2.Fusion Scheme
The source images are first decomposed into multi-scale and multi-directional images using
CTSFL, and then these decomposed images are fused based on a certain fusion scheme. Since the
low frequency parts include mostly the background information, we need weighted-average
operators. In other words, the low frequency coefficients are weight-averaged. On the other hand,
the high frequency parts include mostly the image representation information, such as edge and
texture information. The correlation between a pixel and its neighboring pixels is often larger than
others. Hence, the fusion scheme should know about the region where the pixel is the center. The
fusion scheme computes the region energy of the center pixel and its neighboring pixels. The
expression of region energy is as follows:
2
( , )
( , ) ( , ) ( , )
A A
m n w
E i j m n f m n
∈
= ∑
(1)
2
( , )
( , ) ( , ) ( , )
B B
m n w
E i j m n f m n
∈
= ∑
(2)
, where ,
i k m i k j k n j k
− ≤ ≤ + − ≤ ≤ + , width of the region is ( 2 1)
w w k
= + , ( , )
m n
is the
weighted value and ( , )
f m n is the pixel gray value.
The region energy is larger when the image features are salient; hence, the region energy for each
pixel is compared and high frequency coefficients of the pixel with larger region energy are
selected to be used as high frequency coefficients of the fused image. This way, the salient image
features, such as edge and texture information can be preserved [14, 15].
Based on the above discussion, the region-based measure fusion operators are used in the
experiments. First, the region is selected using a 3-by-3 window. Second, if the difference of the
region energy of pixels from the source images is large, select high frequency coefficients of the
pixel with larger region energy. Otherwise, perform weighted-average over the pixels’ high
frequency coefficients as follows:
( , ) ( , )
( , ) ( , )
1 2
( , ), , ( , )
( , ) ( , ), , ( , )
( , ) ( , ), ( , )
A i j B i j AB
A i j B i j AB
AB
A i j E E R i j T
F i j B i j E E R i j T
A i j B i j R i j T
> >
= < >
+ ≤
(3)
where T is the energy match degree threshold and 1 2 1
+ = . The energy matching degree
( , )
AB
R i j as follows:
( , )
2 ( , ) ( , ) ( , )
( , )
( , ) ( , )
A B
m n w
AB
A B
m n f m n f m n
R i j
E i j E i j
∈
=
+
∑
(4)
6. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
28
where ( , )
AB
R i j value is between 0 and 1.
According to this fusion scheme, the high frequency coefficients of the salient feature region can
be preserved. Lastly, the inverse transform is performed to reconstruct the fused image [15].
5. EXPERIMENTAL STUDY AND ANALYSIS
In our experiments, three different datasets are used: i) a pair of multispectral (MS) and
panchromatic (PAN) images, and ii) two pairs of hyperspectral (HS) and panchromatic (PAN)
images. Each of the dataset is explained more in detail in subsections 5.1 and 5.2 respectively.
Moreover, the following sub-sections explain how the pre-processing is performed over the
source images prior to the image fusion.
In order to quantitatively analyze the fusion results, we employ various quality metrics, and they
can be classified into two categories: i) spectral analysis and ii) spatial analysis. Correlation
coefficient (CC) [16], relative average spectral error (RASE) [17], spectral angle mapper (SAM)
[18] and spectral information divergence (SID) [19] are used for spectral analysis. On the other
hand, for spatial analysis, we employ entropy (E) [20, 21], universal image quality index (UIQI)
[22], signal-to-noise ratio (SNR) and average gradient (AG) [23].
5.1. Multispectral and Panchromatic Images (Dataset 1)
The first dataset was downloaded from [24]. This is a set of 4m-MS and 1m-PAN images of the
city of Fredericton, Canada which were acquired by the commercial satellite IKONOS. PAN
image is resampled to match the size of MS image, and it is assumed that they are co-registered to
each other. Since we do not have a reference image to be compared to the fusion results, we
spatially degraded the original MS to obtain a source image. In other words, we use the degraded
MS image as a source image and the original MS image as a reference image. The fusion results
are quantitatively compared to the original MS image to perform both spectral and spatial
analyses.
Two source images (PAN and MS) and the reference MS image are shown in Figure 7.
(a) (b) (c)
Figure 7.Dataset 1 – the original MS image and two synthesized source images. (a) Original (reference) MS
image. (b) Synthesized PAN source image. (c) Synthesized MS source image
Fusion of the above source images is performed based on CTSFL and fusion scheme as discussed
in the previous section. The fused result is analyzed using various quality metrics as discussed
8. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
30
(a) (b) (c)
Figure 9.Dataset 2 – the original HS image and two synthesized source images. (a) Original (reference) HS
image. (b) Synthesized PAN source image. (c) Synthesized HS source image
(a) (b) (c)
Figure 10.Fusion results of dataset 2. (a) SWT. (b) AWT. (c) CTSFL
Table 2.A performance comparison of the fusion results using quality assessment metrics for Dataset 2.
Fusion
Method
Spectral Analysis Spatial Analysis
CC RASE SAM SID E UIQI SNR AG
SWT 0.821 43.834 0.257 0.187 6.593 0.774 68.654 5.383
AWT 0.849 43.682 0.225 0.168 6.752 0.782 69.136 5.816
CTSFL 0.875 43.426 0.202 0.143 6.851 0.843 72.773 6.732
9. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
31
(a) (b) (c)
Figure 11.Dataset 3 – the original HS image and two synthesized source images. (a) Original (reference)
HS image. (b) Synthesized PAN source image. (c) Synthesized HS source image
(a) (b) (c)
Figure 12.Fusion results of dataset 3. (a) SWT. (b) AWT. (c) CTSFL
10. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
32
Table 3.A performance comparison of the fusion results using quality assessment metrics for Dataset 3.
Fusion
Method
Spectral Analysis Spatial Analysis
CC RASE SAM SID E UIQI SNR AG
SWT 0.792 43.942 0.251 0.198 6.383 0.754 67.459 5.374
AWT 0.828 43.783 0.238 0.184 6.542 0.768 68.362 5.726
CTSFL 0.875 43.711 0.212 0.157 6.731 0.832 71.837 6.375
Similar to the first experiment with dataset 1, the experiments with dataset 2 and 3 show that our
proposed method performs well and its effectiveness as a fusion method is validated. However,
there is a notable difference between the results of dataset 1 and dataset 2 & 3. In other words,
unlike dataset 1, CTSFL outperforms SWT and AWT both spectrally and spatially based on eight
different quality metrics when using dataset 2 and 3. With the dataset 1, AWT seems to be
performing better than CTSFL in terms of CC and RASE; however, this may have happened
because of the way the source images were pre-processed. Dataset 2 and 3 were pre-processed to
obtain two perfectly co-registered source images; on the other hand, dataset 1 was pre-processed
differently assuming that the original source images were co-registered to each other.
Table 4.A close comparison of the fusion results of CTSFL.
CTSFL
Spectral Analysis Spatial Analysis
CC RASE SAM SID E UIQI SNR AG
Dataset 1 0.864 43.735 0.216 0.164 6.425 0.823 71.463 6.126
Dataset 2 0.886 43.426 0.202 0.143 6.851 0.843 72.773 6.732
Dataset 3 0.875 43.711 0.212 0.157 6.731 0.832 71.837 6.375
Table 4 summarizes the fusion results of CTSFL on dataset 1, 2 and 3. This table is presented to
provide a comparison of the fusion results of CTSFL alone. As shown in the table, we can
observe that the results on dataset 2 and 3 are better than the results on dataset 1, and the reason is
because the pre-processing was done differently. In other words, unlike the dataset 1, the original
hyperspectral image was pre-processed to obtain two synthesized source images (panchromatic
and hyperspectral) that were perfectly co-registered. Moreover, if we take a closer look at dataset
2 and 3 only, we can observe that dataset 2 shows better results than dataset 3 in terms of all the
quality metrics. First, in spectral analysis, dataset 2 has higher correlation coefficient than dataset
3 because the original hyperspectral image in dataset 2 consists of 220 bands while the original
hyperspectral image in dataset 3 includes only 12 bands. Therefore, dataset 2 showed better
spectral results by preserving more spectral information with higher correlation between the
bands. Second, in spatial analysis, the fusion results on dataset 2 are better than the results on
dataset 3. The reason is because in both datasets, the original hyperspectral image was spectrally
integrated to obtain a high-resolution panchromatic image as a source image. However, due to a
difference in number of the spectral bands of the original hyperspectral image, the spatial details
were preserved well in dataset 2. As a result, the spatial performance of the fusion results using
dataset 2 was higher.
11. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
33
Table 5.A comparison of the fusion results with different levels of decomposition.
Decomposition
Level
Spectral Analysis Spatial Analysis
CC RASE SAM SID E UIQI SNR AG
Level 1 0.746 47.357 0.256 0.234 6.225 0.632 69.463 5.926
Level 2 0.823 45.264 0.232 0.193 6.451 0.734 70.773 6.132
Level 3 0.875 43.711 0.212 0.157 6.731 0.832 71.837 6.375
Level 4 0.819 46.925 0.247 0.221 6.158 0.779 68.962 6.021
Since it has been verified that the CTSFL outperforms other wavelet-based methods in fusing
remote sensing images, we have analyzed one more aspect of the image fusion which is the
decomposition level. Dataset 3 was used to analyze how the decomposition level affects the
fusion results. As Table 5 shows, the decomposition level of 3 provided the best fusion result,
compared to the levels that are smaller or greater than the level 3. Further studies are necessary on
this topic of the decomposition level because the analysis results may vary based on which type
or modality of dataset is used in the fusion.
Table 6.An analysis on the importance of the image registration.
Image
Registration
Spectral Analysis Spatial Analysis
CC RASE SAM SID E UIQI SNR AG
Case 1 0.582 52.569 0.458 0.364 5.925 0.502 67.643 5.716
Case 2 0.695 47.465 0.364 0.293 6.451 0.634 70.336 6.032
Case 3 0.755 46.731 0.312 0.237 6.613 0.683 71.748 6.457
Case 4 0.834 43.925 0.206 0.143 6.858 0.877 74.692 7.021
Another important comparative analysis was conducted on the registration of the source (input)
images using Dataset 3. Most of the remote sensing image fusion methods that are being studied
today mainly focus on the fusion methods and rules. In other words, they fail to notice how
important the registration of the source images is. Therefore, as shown in Table 6, four different
cases have been compared and analyzed. In Case 1, two purposely misaligned source images are
fused together. The source images were shifted horizontally and no image registration was
performed here in order to produce the worst scenario. In Case 2, the same set of source images
was used; however, we applied intensity-based image registration to align the source images.
Intensity-based registration aligns two images by comparing the intensity patterns in each image
via correlation metrics. In Case 3, the same set of source images was used; however, the feature-
based image registration was applied to align the source images. Feature-based registration aligns
two images by finding correspondence between the image features. In our case, we utilized the
edge features that were extracted from the source images. For a point on the first image, it is
expected to find the best matching point on the second image according to the edge features. Case
4 was the same as the previous experiments where two perfectly co-registered source images
were used. As we can see from Table 6, Case 4 produced the best fusion results since the source
images were perfectly registered to each other, and Case 1 obviously produced the worst fusion
results since no registration was performed to the source images. From the comparison between
Case 2 and Case 3, we observed that Case 3 produced better fusion results because the feature-
based image registration performed better in aligning the source images by extracting the edge
information.
12. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
34
6. CONCLUSIONS
In this paper, we proposed a new fusion method based on contourlet transform with sharp
frequency localization. Recently, contourlet transform has been utilized by many researchers in
the field of image fusion due to its advantages over conventional wavelet-based methods.
However, the contourlet transform has a major drawback, which is that its basis images are not
localized in the frequency domain. A new method of constructing the contourlet transform to
overcome the drawback is CTSFL, and this new idea was implemented in our fusion method.
Fusion results of the proposed method were compared to well-known wavelet-based fusion
methods, and the results were quantitatively analyzed using various quality metrics both spatially
and spectrally. The performance comparison results show that our method outperforms the other
two wavelet-based methods both spatially and spectrally; hence, the effectiveness of our fusion
method is validated. Moreover, the effects of the decomposition level were tested and analyzed
using Dataset 3. The comparison results show that the decomposition level of 3 provides better
fusion results in terms of remote sensing image fusion. However, it is necessary to study this
topic more in detail with various modalities because the fusion results may vary according to the
modality of the dataset. It was also observed that the registration of the source images played an
important role in the fusion process. Based on our experimental analysis, the feature-based image
registration produced better fusion results than the intensity-based image registration. The effect
and dependency of the source image pre-processing need to be further studied, and this area may
be a good future research in remote sensing image fusion.
ACKNOWLEDGEMENTS
This work was supported in part by the Nevada EPSCoR program NSF award #EPS-IIA-1301726
and by the DOD-DTRAGrant/Award #: HDTRA1-12-1-0033.
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