The Experts below are selected from a list of 28338 Experts worldwide ranked by ideXlab platform
Halil Berberoglu - One of the best experts on this subject based on the ideXlab platform.
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Rapid algal culture diagnostics for open ponds using Multispectral Image analysis.
Biotechnology progress, 2013Co-Authors: Thomas E. Murphy, Keith Macon, Halil BerberogluAbstract:This paper presents a Multispectral Image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel oating reference platform was placed in the culture, which enhanced the sensitivity of Image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture Images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22% and 14%, respectively. Moreover, the dierence in spectral signatures between the two
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Rapid algal culture diagnostics for open ponds using Multispectral Image analysis.
Biotechnology progress, 2013Co-Authors: Thomas E. Murphy, Keith Macon, Halil BerberogluAbstract:This article presents a Multispectral Image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel floating reference platform was placed in the culture, which enhanced the sensitivity of Image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture Images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22 and 14%, respectively. Moreover, the difference in spectral signatures between the two strains was exploited to detect the invasion of Chlorella sp. cultures by A. variabilis. Invasion was successfully detected for A. variabilis to Chlorella sp. mass ratios as small as 0.08. Finally, a method was presented for using Multispectral imaging to detect thermal stress in A. variabilis. These methods can be extended to field applications to provide delay free process control feedback for efficient operation of large scale algae cultivation systems.
Hui-liang Shen - One of the best experts on this subject based on the ideXlab platform.
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Multispectral Image Super-Resolution via RGB Image Fusion and Radiometric Calibration
IEEE Transactions on Image Processing, 2019Co-Authors: Zhi-wei Pan, Hui-liang ShenAbstract:Multispectral imaging is of wide application for its capability in acquiring the spectral information of scenes. Due to hardware limitation, Multispectral imaging device usually cannot achieve high-spatial resolution. To address the issue, this paper proposes a Multispectral Image super-resolution algorithm, referred as SRIF, by fusing the low-resolution Multispectral Image and the high-resolution (HR) RGB Image. It deals with the general circumstance that Image intensity is linear to scene radiance for Multispectral imaging devices while is nonlinear and unknown for most RGB cameras. The SRIF algorithm first solves the inverse camera response function and a spectral sensitivity function of RGB camera, and establishes the linear relationship between Multispectral and RGB Images. Then the unknown HR Multispectral Image is efficiently reconstructed according to the linear Image degradation models. Meanwhile, the edge structure of the reconstructed HR Multispectral Image is kept in accordance with that of the RGB Image using a weighted total variation regularizer. The effectiveness of the SRIF algorithm is evaluated on both public datasets and our Image set. Experimental results validate that the SRIF algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.
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PRCV (1) - Multispectral Image Super-Resolution Using Structure-Guided RGB Image Fusion
Pattern Recognition and Computer Vision, 2018Co-Authors: Zhi-wei Pan, Hui-liang ShenAbstract:Due to hardware limitation, Multispectral imaging device usually cannot achieve high spatial resolution. To address the issue, this paper proposes a Multispectral Image super-resolution algorithm by fusing the low-resolution Multispectral Image and the high-resolution RGB Image. The fusion is formulated as an optimization problem according to the linear Image degradation models. Meanwhile, the fusion is guided by the edge structure of RGB Image via the directional total variation regularizer. Then the fusion problem is solved by the alternating direction method of multipliers algorithm through iteration. The subproblems in each iterative step is simple and can be solved in closed-form. The effectiveness of the proposed algorithm is evaluated on both public datasets and our Image set. Experimental results validate that the algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.
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normalized total gradient a new measure for Multispectral Image registration
IEEE Transactions on Image Processing, 2018Co-Authors: Shujie Chen, Hui-liang Shen, John Haozhong XinAbstract:Image registration is a fundamental issue in Multispectral Image processing, and is challenged by two main characteristics of Multispectral Images. First, the regional intensities can be essentially different between band Images. Second, the local contrasts of two difference band Images are inconsistent or even reversed. Conventional measures can align Images with different regional intensity levels, but may fail in the circumstance of severe local intensity variation. In this paper, a new measure called normalized total gradient is proposed for Multispectral Image registration. The measure is based on the key assumption (observation) that the gradient of the difference between two aligned band Images is sparser than that between two misaligned ones. A registration framework, which incorporates Image pyramid and global/local optimization, is further introduced for affine transform. Experimental results validate that the proposed method is not only effective for Multispectral Image registration, but also applicable to general unimodal/multimodal Image registration tasks. It performs better than or comparable to the existing methods, both quantitatively and qualitatively.
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ICIP - Multispectral Image compression by cluster-adaptive subspace representation
2010 IEEE International Conference on Image Processing, 2010Co-Authors: Hui-liang Shen, John Haozhong XinAbstract:Multispectral imaging has attracted much interest in color science area, for its ability in providing much more spectral information than 3-channel color Images. Due to the huge data volume, it is necessary to compress Multispectral Images for efficient transmission. This paper proposes a framework for spectral compression of Multispectral Image by using cluster-adaptive subspaces representation. In the framework, Multispectral Image is initially segmented by hierarchical analysis of the transform coefficients in the global subspace, and then ambiguous pixels are identified and classified into proper clusters based on linear discriminant analysis. The dimensionality of each adaptive subspace is determined by specified reconstruction error level, followed by further cluster splitting if necessary. The efficiency of the proposed method is verified by experiments on real Multispectral Images.
Thomas E. Murphy - One of the best experts on this subject based on the ideXlab platform.
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Rapid algal culture diagnostics for open ponds using Multispectral Image analysis.
Biotechnology progress, 2013Co-Authors: Thomas E. Murphy, Keith Macon, Halil BerberogluAbstract:This paper presents a Multispectral Image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel oating reference platform was placed in the culture, which enhanced the sensitivity of Image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture Images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22% and 14%, respectively. Moreover, the dierence in spectral signatures between the two
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Rapid algal culture diagnostics for open ponds using Multispectral Image analysis.
Biotechnology progress, 2013Co-Authors: Thomas E. Murphy, Keith Macon, Halil BerberogluAbstract:This article presents a Multispectral Image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel floating reference platform was placed in the culture, which enhanced the sensitivity of Image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture Images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22 and 14%, respectively. Moreover, the difference in spectral signatures between the two strains was exploited to detect the invasion of Chlorella sp. cultures by A. variabilis. Invasion was successfully detected for A. variabilis to Chlorella sp. mass ratios as small as 0.08. Finally, a method was presented for using Multispectral imaging to detect thermal stress in A. variabilis. These methods can be extended to field applications to provide delay free process control feedback for efficient operation of large scale algae cultivation systems.
Keith Macon - One of the best experts on this subject based on the ideXlab platform.
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Rapid algal culture diagnostics for open ponds using Multispectral Image analysis.
Biotechnology progress, 2013Co-Authors: Thomas E. Murphy, Keith Macon, Halil BerberogluAbstract:This paper presents a Multispectral Image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel oating reference platform was placed in the culture, which enhanced the sensitivity of Image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture Images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22% and 14%, respectively. Moreover, the dierence in spectral signatures between the two
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Rapid algal culture diagnostics for open ponds using Multispectral Image analysis.
Biotechnology progress, 2013Co-Authors: Thomas E. Murphy, Keith Macon, Halil BerberogluAbstract:This article presents a Multispectral Image analysis approach for probing the spectral backscattered irradiance from algal cultures. It was demonstrated how this spectral information can be used to measure algal biomass concentration, detect invasive species, and monitor culture health in real time. To accomplish this, a conventional RGB camera was used as a three band photodetector for imaging cultures of the green alga Chlorella sp. and the cyanobacterium Anabaena variabilis. A novel floating reference platform was placed in the culture, which enhanced the sensitivity of Image color intensity to biomass concentration. Correlations were generated between the RGB color vector of culture Images and the biomass concentrations for monocultures of each strain. These correlations predicted the biomass concentrations of independently prepared cultures with average errors of 22 and 14%, respectively. Moreover, the difference in spectral signatures between the two strains was exploited to detect the invasion of Chlorella sp. cultures by A. variabilis. Invasion was successfully detected for A. variabilis to Chlorella sp. mass ratios as small as 0.08. Finally, a method was presented for using Multispectral imaging to detect thermal stress in A. variabilis. These methods can be extended to field applications to provide delay free process control feedback for efficient operation of large scale algae cultivation systems.
Zhi-wei Pan - One of the best experts on this subject based on the ideXlab platform.
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Multispectral Image Super-Resolution via RGB Image Fusion and Radiometric Calibration
IEEE Transactions on Image Processing, 2019Co-Authors: Zhi-wei Pan, Hui-liang ShenAbstract:Multispectral imaging is of wide application for its capability in acquiring the spectral information of scenes. Due to hardware limitation, Multispectral imaging device usually cannot achieve high-spatial resolution. To address the issue, this paper proposes a Multispectral Image super-resolution algorithm, referred as SRIF, by fusing the low-resolution Multispectral Image and the high-resolution (HR) RGB Image. It deals with the general circumstance that Image intensity is linear to scene radiance for Multispectral imaging devices while is nonlinear and unknown for most RGB cameras. The SRIF algorithm first solves the inverse camera response function and a spectral sensitivity function of RGB camera, and establishes the linear relationship between Multispectral and RGB Images. Then the unknown HR Multispectral Image is efficiently reconstructed according to the linear Image degradation models. Meanwhile, the edge structure of the reconstructed HR Multispectral Image is kept in accordance with that of the RGB Image using a weighted total variation regularizer. The effectiveness of the SRIF algorithm is evaluated on both public datasets and our Image set. Experimental results validate that the SRIF algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.
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PRCV (1) - Multispectral Image Super-Resolution Using Structure-Guided RGB Image Fusion
Pattern Recognition and Computer Vision, 2018Co-Authors: Zhi-wei Pan, Hui-liang ShenAbstract:Due to hardware limitation, Multispectral imaging device usually cannot achieve high spatial resolution. To address the issue, this paper proposes a Multispectral Image super-resolution algorithm by fusing the low-resolution Multispectral Image and the high-resolution RGB Image. The fusion is formulated as an optimization problem according to the linear Image degradation models. Meanwhile, the fusion is guided by the edge structure of RGB Image via the directional total variation regularizer. Then the fusion problem is solved by the alternating direction method of multipliers algorithm through iteration. The subproblems in each iterative step is simple and can be solved in closed-form. The effectiveness of the proposed algorithm is evaluated on both public datasets and our Image set. Experimental results validate that the algorithm outperforms the state-of-the-arts in terms of both reconstruction accuracy and computational efficiency.
