The Experts below are selected from a list of 5001 Experts worldwide ranked by ideXlab platform
Michel Jourlin - One of the best experts on this subject based on the ideXlab platform.
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REGION HOMOGENEITY IN THE Logarithmic Image PROCESSING FRAMEWORK: APPLICATION TO REGION GROWING ALGORITHMS
Image Analysis & Stereology, 2019Co-Authors: Guillaume Noyel, Michel JourlinAbstract:In order to create an Image segmentation method robust to lighting changes, two novel homogeneity criteria of an Image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to variations of the object thickness or opacity. Each criterion is then applied in Revol and Jourlin’s (1997) region growing method which is based on the homogeneity of an Image region. The region growing method becomes therefore robust to the lighting changes specific to each criterion. Experiments on simulated and on real Images presenting lighting variations prove the robustness of the criteria to those variations. Compared to a state-of the art method based on the Image component-tree, ours is more robust. These results open the way to numerous applications where the lighting is uncontrolled or partially controlled.
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Homogeneity of a region in the Logarithmic Image processing framework: application to region growing algorithms
2018Co-Authors: Michel Jourlin, Guillaume NoyelAbstract:The current paper deals with the role played by Logarithmic Image Processing (LIP) operators for evaluating the homogeneity of a region. Two new criteria of heterogeneity are introduced, one based on the LIP addition and the other based on the LIP scalar multiplication. Such tools are able to manage Region Growing algorithms following the Revol's technique: starting from an initial seed, they consist of applying specific dilations to the growing region while its inhomogeneity level does not exceed a certain level. The new approaches we introduce are significantly improving Revol's existing technique by making it robust to contrast variations in Images. Such a property strongly reduces the chaining effect arising in region growing processes.
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a simple expression for the map of asplund s distances with the multiplicative Logarithmic Image processing lip law
12th European Congress for Stereology and Image Analysis 2017, 2017Co-Authors: Guillaume Noyel, Michel JourlinAbstract:We introduce a simple expression for the map of Asplund's distances with the multiplicative Logarithmic Image Processing (LIP) law. It is a difference between a morphological dilation and a morphological erosion with an additive structuring function which corresponds to a morphological gradient.
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double sided probing by map of asplund s distances using Logarithmic Image processing in the framework of mathematical morphology
International Symposium on Memory Management, 2017Co-Authors: Guillaume Noyel, Michel JourlinAbstract:We establish the link between Mathematical Morphology and the map of Asplund's distances between a probe and a grey scale function, using the Logarithmic Image Processing scalar multiplication. We demonstrate that the map is the logarithm of the ratio between a dilation and an erosion of the function by a structuring function: the probe. The dilations and erosions are mappings from the lattice of the Images into the lattice of the positive functions. Using a flat structuring element, the expression of the map of Asplund's distances can be simplified with a dilation and an erosion of the Image; these mappings stays in the lattice of the Images. We illustrate our approach by an example of pattern matching with a non-flat structuring function.
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ISMM - Double-sided probing by map of Asplund's distances using Logarithmic Image Processing in the framework of Mathematical Morphology
Lecture Notes in Computer Science, 2017Co-Authors: Guillaume Noyel, Michel JourlinAbstract:We establish the link between Mathematical Morphology and the map of Asplund's distances between a probe and a grey scale function, using the Logarithmic Image Processing scalar multiplication. We demonstrate that the map is the logarithm of the ratio between a dilation and an erosion of the function by a structuring function: the probe. The dilations and erosions are mappings from the lattice of the Images into the lattice of the positive functions. Using a flat structuring element, the expression of the map of Asplund's distances can be simplified with a dilation and an erosion of the Image; these mappings stays in the lattice of the Images. We illustrate our approach by an example of pattern matching with a non-flat structuring function.
Guillaume Noyel - One of the best experts on this subject based on the ideXlab platform.
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REGION HOMOGENEITY IN THE Logarithmic Image PROCESSING FRAMEWORK: APPLICATION TO REGION GROWING ALGORITHMS
Image Analysis & Stereology, 2019Co-Authors: Guillaume Noyel, Michel JourlinAbstract:In order to create an Image segmentation method robust to lighting changes, two novel homogeneity criteria of an Image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to variations of the object thickness or opacity. Each criterion is then applied in Revol and Jourlin’s (1997) region growing method which is based on the homogeneity of an Image region. The region growing method becomes therefore robust to the lighting changes specific to each criterion. Experiments on simulated and on real Images presenting lighting variations prove the robustness of the criteria to those variations. Compared to a state-of the art method based on the Image component-tree, ours is more robust. These results open the way to numerous applications where the lighting is uncontrolled or partially controlled.
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Homogeneity of a region in the Logarithmic Image processing framework: application to region growing algorithms
2018Co-Authors: Michel Jourlin, Guillaume NoyelAbstract:The current paper deals with the role played by Logarithmic Image Processing (LIP) operators for evaluating the homogeneity of a region. Two new criteria of heterogeneity are introduced, one based on the LIP addition and the other based on the LIP scalar multiplication. Such tools are able to manage Region Growing algorithms following the Revol's technique: starting from an initial seed, they consist of applying specific dilations to the growing region while its inhomogeneity level does not exceed a certain level. The new approaches we introduce are significantly improving Revol's existing technique by making it robust to contrast variations in Images. Such a property strongly reduces the chaining effect arising in region growing processes.
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a simple expression for the map of asplund s distances with the multiplicative Logarithmic Image processing lip law
12th European Congress for Stereology and Image Analysis 2017, 2017Co-Authors: Guillaume Noyel, Michel JourlinAbstract:We introduce a simple expression for the map of Asplund's distances with the multiplicative Logarithmic Image Processing (LIP) law. It is a difference between a morphological dilation and a morphological erosion with an additive structuring function which corresponds to a morphological gradient.
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double sided probing by map of asplund s distances using Logarithmic Image processing in the framework of mathematical morphology
International Symposium on Memory Management, 2017Co-Authors: Guillaume Noyel, Michel JourlinAbstract:We establish the link between Mathematical Morphology and the map of Asplund's distances between a probe and a grey scale function, using the Logarithmic Image Processing scalar multiplication. We demonstrate that the map is the logarithm of the ratio between a dilation and an erosion of the function by a structuring function: the probe. The dilations and erosions are mappings from the lattice of the Images into the lattice of the positive functions. Using a flat structuring element, the expression of the map of Asplund's distances can be simplified with a dilation and an erosion of the Image; these mappings stays in the lattice of the Images. We illustrate our approach by an example of pattern matching with a non-flat structuring function.
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ISMM - Double-sided probing by map of Asplund's distances using Logarithmic Image Processing in the framework of Mathematical Morphology
Lecture Notes in Computer Science, 2017Co-Authors: Guillaume Noyel, Michel JourlinAbstract:We establish the link between Mathematical Morphology and the map of Asplund's distances between a probe and a grey scale function, using the Logarithmic Image Processing scalar multiplication. We demonstrate that the map is the logarithm of the ratio between a dilation and an erosion of the function by a structuring function: the probe. The dilations and erosions are mappings from the lattice of the Images into the lattice of the positive functions. Using a flat structuring element, the expression of the map of Asplund's distances can be simplified with a dilation and an erosion of the Image; these mappings stays in the lattice of the Images. We illustrate our approach by an example of pattern matching with a non-flat structuring function.
Sos S Agaian - One of the best experts on this subject based on the ideXlab platform.
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Image Fusion Using a Parameterized Logarithmic Image Processing Framework
Image Fusion, 2011Co-Authors: Sos S Agaian, Karen Panetta, Shahan NercessianAbstract:Advances in sensor technology have brought about extensive research in the field of Image fusion. Image fusion is the combination of two or more source Images which vary in resolution, instrument modality, or Image capture technique into a single composite representation (Hill et al., 2002). Thus, the source Images are complementary in many ways, with no one input Image being an adequate data representation of the scene. Therefore, the goal of an Image fusion algorithm is to integrate the redundant and complementary information obtained from the source Images in order to form a new Image which provides a better description of the scene for human or machine perception (Kumar & Dass, 2009). Image fusion is essential for computer vision and robotics systems in which fusion results can be used to aid further processing steps for a given task. Image fusion techniques are practical and fruitful for many applications, including medical imaging, security, military, remote sensing, digital camera and consumer use. There are many cases in medical imaging where viewing a series of Images individually is not convenient. For example, magnetic resonance imaging (MRI) and computed tomography (CT) Images provide structural and anatomical information with high resolution. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) Images provide functional information with low resolution. Therefore, the fusion of MRI or CT Images with PET or SPECT Images can provide the needed structural, anatomical, and functional information for medical diagnosis, anomaly detection and quantitative analysis (Daneshvar & Ghassemian, 2010). Moreover, the combination of MRI and CT Images can provide Images containing both dense bone structure and soft tissue information (Yang et al., 2010). Similarly, the combination of MRI-T1 Images provides greater details of anamotical structures while MRI-T2 Images provides greater contrast between normal and abmormal tissue matter, and thus, their fusion can also help to extract the features needed by physicians (Wang, 2008). In security applications, thermal/infrared Images provide information regarding the presence of intruders or potential threat objects (Zhang & Blum, 1997). For military applications, such Images can also provide terrain clues for helicopter navigation. Visible light Images provide high-resolution structural information based on the way in which light is reflected. Thus, the fusion of thermal/infrared and visible Images can be used to aid navigation, concealed weapon detection, and surveillance/border patrol by
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multiresolution decomposition schemes using the parameterized Logarithmic Image processing model with application to Image fusion
EURASIP Journal on Advances in Signal Processing, 2011Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:New pixel- and region-based multiresolution Image fusion algorithms are introduced in this paper using the Parameterized Logarithmic Image Processing (PLIP) model, a framework more suitable for processing Images. A mathematical analysis shows that the Logarithmic Image Processing (LIP) model and standard mathematical operators are extreme cases of the PLIP model operators. Moreover, the PLIP model operators also have the ability to take on cases in between LIP and standard operators based on the visual requirements of the input Images. PLIP-based multiresolution decomposition schemes are developed and thoroughly applied for Image fusion as analysis and synthesis methods. The new decomposition schemes and fusion rules yield novel Image fusion algorithms which are able to provide visually more pleasing fusion results. LIP-based multiresolution Image fusion approaches are consequently formulated due to the generalized nature of the PLIP model. Computer simulations illustrate that the proposed Image fusion algorithms using the Parameterized Logarithmic Laplacian Pyramid, Parameterized Logarithmic DiscreteWavelet Transform, and Parameterized Logarithmic Stationary Wavelet Transform outperform their respective traditional approaches by both qualitative and quantitative means. The algorithms were tested over a range of different Image classes, including out-of-focus, medical, surveillance, and remote sensing Images.
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multi scale Image fusion using the parameterized Logarithmic Image processing model
Systems Man and Cybernetics, 2010Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:Image fusion is the process of combining multiple Images into a single Image which retains the most pertinent information from each original Image source. More recently, multi-scale Image fusion approaches have emerged as a means of providing a more meaningful fusion which better reflects the human visual system. In this paper, multi-scale decomposition techniques and Image fusion algorithms are adapted using the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear Image processing framework which more accurately processes Images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.
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SMC - Multi-scale Image fusion using the Parameterized Logarithmic Image Processing model
2010 IEEE International Conference on Systems Man and Cybernetics, 2010Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:Image fusion is the process of combining multiple Images into a single Image which retains the most pertinent information from each original Image source. More recently, multi-scale Image fusion approaches have emerged as a means of providing a more meaningful fusion which better reflects the human visual system. In this paper, multi-scale decomposition techniques and Image fusion algorithms are adapted using the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear Image processing framework which more accurately processes Images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.
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Image fusion using the Parameterized Logarithmic Dual Tree Complex Wavelet Transform
2010 IEEE International Conference on Technologies for Homeland Security (HST), 2010Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:Image fusion combines multiple Images into a single Image containing the relevant information from each of the original source Images. This paper introduces a new Parameterized Logarithmic Dual Tree Complex Wavelet Transform (PL-DT-CWT) and its application for Image fusion. The new transform combines the Dual Tree Complex Wavelet Transform (DT-CWT) with the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear Image processing framework for processing Images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.
Jean-charles Pinoli - One of the best experts on this subject based on the ideXlab platform.
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the color Logarithmic Image processing colip antagonist space and chromaticity diagram
Computational Color Imaging Workshop, 2015Co-Authors: Yann Gavet, Johan Debayle, Jean-charles PinoliAbstract:The CoLIP framework defines a vectorial space for color Images. It is in accordance with the theories of visual perception (Weber, Fechner) as well as Hering’s trichromacy theory. It is mathematically well defined and computationally usable. This article recalls the fundamentals of the LIP framework for graytone Images, and introduces the elementary operations of the vectorial structure for color Images. It illutrates the representation of the chromaticity diagram with color modification application, namely white balance correction and color transfer. The results show that the hull of the diagram are not modified, but the colors are.
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CCIW - The Color Logarithmic Image Processing (CoLIP) antagonist space and chromaticity diagram
Lecture Notes in Computer Science, 2015Co-Authors: Yann Gavet, Johan Debayle, Jean-charles PinoliAbstract:The CoLIP framework defines a vectorial space for color Images. It is in accordance with the theories of visual perception (Weber, Fechner) as well as Hering’s trichromacy theory. It is mathematically well defined and computationally usable. This article recalls the fundamentals of the LIP framework for graytone Images, and introduces the elementary operations of the vectorial structure for color Images. It illutrates the representation of the chromaticity diagram with color modification application, namely white balance correction and color transfer. The results show that the hull of the diagram are not modified, but the colors are.
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The Color Logarithmic Image Processing (CoLIP) antagonist space.
2015Co-Authors: Yann Gavet, Johan Debayle, Jean-charles PinoliAbstract:The Color Logarithmic Image Processing (CoLIP) is a mathematical framework for the representation and processing of color Images. It is psychophysically well justified since it is consistent with several human visual perception laws and characteristics. It is mathematically and computationally relevant since it allows to consider color Images as vectors in an abstract linear space, contrary to the classical color spaces (e.g., RGB and L∗a∗b∗). The first purpose of this book chapter is to present the mathematical fundamentals of the CoLIP together with its main psychophysical connections (Grasmann’s law, color matching functions, chromaticity diagram and the Maxwell triangle). The second purpose is to present some basic Image processing and analysis techniques for contrast enhancement (histogram equalization, dynamic range maximization, toggle contrast calculation), white balance correction, color transfer, K-means clustering and filtering. Most of them are applied on various original color Images in a comparative way between CoLIP, RGB and L∗a∗b∗ color spaces.
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Color Correction in the Framework of Color Logarithmic Image Processing
2011Co-Authors: Hélène Gouinaud, Yann Gavet, Johan Debayle, Jean-charles PinoliAbstract:The Logarithmic Image Processing (LIP) approach is a mathematical framework developed for the representation and processing of Images valued in a bounded intensity range. The LIP theory is physically and psychophysically well justified since it is consistent with several laws of human brightness perception and with the multiplicative Image formation model. In this paper, the so-called Color Logarithmic Image Processing (CoLIP) framework is introduced. This novel framework expands the LIP theory to color Images in the context of the human color visual perception. Color Images are represented by their color tone functions that can be combined by means of basic operations, addition, scalar multiplication and subtraction, opening new pathways for color Image processing. In order to highlight the CoLIP relevance with color constancy, a color correction method based on the subtraction is proposed and tested on CoLIP approach and Logarithmic hUe eXtension (LUX) approach, also based on the LIP theory, on differently illuminated Images: underwater Images with a blue illuminant, and indoor Images with yellow illuminant.
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Improving focus measurements using Logarithmic Image processing
Journal of microscopy, 2010Co-Authors: Mathieu Fernandes, Yann Gavet, Jean-charles PinoliAbstract:The Logarithmic Image processing (LIP) model is a mathematical framework which provides algebraic and functional operations for the processing of intensity Images valued in a bounded range. The LIP model has been proved to be physically consistent, most notably with some Image formation models and several laws and characteristics of human brightness perception. This paper addresses the Image focus measurement problem using the LIP model. The three most classical Image focus measurements: the sum-modified-Laplacian, the tenengrad and the variance, which aim at estimating the degree of focus of an acquired Image by emphasizing and quantifying its sharpness information, are considered and reinterpreted using the LIP framework. These reinterpretations notably make attempts at evaluating degrees of focus in terms of human brightness (sensation) from physical light stimuli. Their potential is illustrated and validated on shape-from-focus issues on both simulated data and real acquisitions in digital optical microscopy. The concept of shape-from-focus involves recovering the shape of an observed thick sample by locally maximizing a focus measurement throughout a sequence of differently focused Images. Finally, it is shown that the LIP-based focus measurements clearly outperform their respective classical ones.
Shahan Nercessian - One of the best experts on this subject based on the ideXlab platform.
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Image Fusion Using a Parameterized Logarithmic Image Processing Framework
Image Fusion, 2011Co-Authors: Sos S Agaian, Karen Panetta, Shahan NercessianAbstract:Advances in sensor technology have brought about extensive research in the field of Image fusion. Image fusion is the combination of two or more source Images which vary in resolution, instrument modality, or Image capture technique into a single composite representation (Hill et al., 2002). Thus, the source Images are complementary in many ways, with no one input Image being an adequate data representation of the scene. Therefore, the goal of an Image fusion algorithm is to integrate the redundant and complementary information obtained from the source Images in order to form a new Image which provides a better description of the scene for human or machine perception (Kumar & Dass, 2009). Image fusion is essential for computer vision and robotics systems in which fusion results can be used to aid further processing steps for a given task. Image fusion techniques are practical and fruitful for many applications, including medical imaging, security, military, remote sensing, digital camera and consumer use. There are many cases in medical imaging where viewing a series of Images individually is not convenient. For example, magnetic resonance imaging (MRI) and computed tomography (CT) Images provide structural and anatomical information with high resolution. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) Images provide functional information with low resolution. Therefore, the fusion of MRI or CT Images with PET or SPECT Images can provide the needed structural, anatomical, and functional information for medical diagnosis, anomaly detection and quantitative analysis (Daneshvar & Ghassemian, 2010). Moreover, the combination of MRI and CT Images can provide Images containing both dense bone structure and soft tissue information (Yang et al., 2010). Similarly, the combination of MRI-T1 Images provides greater details of anamotical structures while MRI-T2 Images provides greater contrast between normal and abmormal tissue matter, and thus, their fusion can also help to extract the features needed by physicians (Wang, 2008). In security applications, thermal/infrared Images provide information regarding the presence of intruders or potential threat objects (Zhang & Blum, 1997). For military applications, such Images can also provide terrain clues for helicopter navigation. Visible light Images provide high-resolution structural information based on the way in which light is reflected. Thus, the fusion of thermal/infrared and visible Images can be used to aid navigation, concealed weapon detection, and surveillance/border patrol by
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multiresolution decomposition schemes using the parameterized Logarithmic Image processing model with application to Image fusion
EURASIP Journal on Advances in Signal Processing, 2011Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:New pixel- and region-based multiresolution Image fusion algorithms are introduced in this paper using the Parameterized Logarithmic Image Processing (PLIP) model, a framework more suitable for processing Images. A mathematical analysis shows that the Logarithmic Image Processing (LIP) model and standard mathematical operators are extreme cases of the PLIP model operators. Moreover, the PLIP model operators also have the ability to take on cases in between LIP and standard operators based on the visual requirements of the input Images. PLIP-based multiresolution decomposition schemes are developed and thoroughly applied for Image fusion as analysis and synthesis methods. The new decomposition schemes and fusion rules yield novel Image fusion algorithms which are able to provide visually more pleasing fusion results. LIP-based multiresolution Image fusion approaches are consequently formulated due to the generalized nature of the PLIP model. Computer simulations illustrate that the proposed Image fusion algorithms using the Parameterized Logarithmic Laplacian Pyramid, Parameterized Logarithmic DiscreteWavelet Transform, and Parameterized Logarithmic Stationary Wavelet Transform outperform their respective traditional approaches by both qualitative and quantitative means. The algorithms were tested over a range of different Image classes, including out-of-focus, medical, surveillance, and remote sensing Images.
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multi scale Image fusion using the parameterized Logarithmic Image processing model
Systems Man and Cybernetics, 2010Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:Image fusion is the process of combining multiple Images into a single Image which retains the most pertinent information from each original Image source. More recently, multi-scale Image fusion approaches have emerged as a means of providing a more meaningful fusion which better reflects the human visual system. In this paper, multi-scale decomposition techniques and Image fusion algorithms are adapted using the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear Image processing framework which more accurately processes Images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.
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SMC - Multi-scale Image fusion using the Parameterized Logarithmic Image Processing model
2010 IEEE International Conference on Systems Man and Cybernetics, 2010Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:Image fusion is the process of combining multiple Images into a single Image which retains the most pertinent information from each original Image source. More recently, multi-scale Image fusion approaches have emerged as a means of providing a more meaningful fusion which better reflects the human visual system. In this paper, multi-scale decomposition techniques and Image fusion algorithms are adapted using the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear Image processing framework which more accurately processes Images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.
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Image fusion using the Parameterized Logarithmic Dual Tree Complex Wavelet Transform
2010 IEEE International Conference on Technologies for Homeland Security (HST), 2010Co-Authors: Shahan Nercessian, Karen Panetta, Sos S AgaianAbstract:Image fusion combines multiple Images into a single Image containing the relevant information from each of the original source Images. This paper introduces a new Parameterized Logarithmic Dual Tree Complex Wavelet Transform (PL-DT-CWT) and its application for Image fusion. The new transform combines the Dual Tree Complex Wavelet Transform (DT-CWT) with the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear Image processing framework for processing Images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.
