The Experts below are selected from a list of 5349 Experts worldwide ranked by ideXlab platform
David C. Munson - One of the best experts on this subject based on the ideXlab platform.
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Synthetic Aperture Radar Autofocus via Semidefinite Relaxation
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2013Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:The Autofocus problem in synthetic aperture radar imaging amounts to estimating unknown phase errors caused by unknown platform or target motion. At the heart of three state-of-the-art Autofocus algorithms, namely, phase gradient Autofocus, multichannel Autofocus (MCA), and Fourier-domain multichannel Autofocus (FMCA), is the solution of a constant modulus quadratic program (CMQP). Currently, these algorithms solve a CMQP by using an eigenvalue relaxation approach. We propose an alternative relaxation approach based on semidefinite programming, which has recently attracted considerable attention in other signal processing problems. Experimental results show that our proposed methods provide promising performance improvements for MCA and FMCA through an increase in computational complexity.
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Fourier-Domain Multichannel Autofocus for Synthetic Aperture Radar
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2011Co-Authors: Kuang-hung Liu, David C. MunsonAbstract:Synthetic aperture radar (SAR) imaging suffers from image focus degradation in the presence of phase errors in the received signal due to unknown platform motion or signal propagation delays. We present a new Autofocus algorithm, termed Fourier-domain multichannel Autofocus (FMCA), that is derived under a linear algebraic framework, allowing the SAR image to be focused in a noniterative fashion. Motivated by the mutichannel Autofocus (MCA) approach, the proposed Autofocus algorithm invokes the assumption of a low-return region, which generally is provided within the antenna sidelobes. Unlike MCA, FMCA works with the collected polar Fourier data directly and is capable of accommodating wide-angle monostatic SAR and bistatic SAR scenarios. Most previous SAR Autofocus algorithms rely on the prior assumption that radar's range of look angles is small so that the phase errors can be modeled as varying along only one dimension in the collected Fourier data. And, in some cases, implicit assumptions are made regarding the SAR scene. Performance of such Autofocus algorithms degrades if the assumptions are not satisfied. The proposed algorithm has the advantage that it does not require prior assumptions about the range of look angles, nor characteristics of the scene.
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ICASSP - Maximum likelihood SAR Autofocus with low-return region
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:Autofocus algorithms deal with image restoration in a nonideal synthetic aperture radar (SAR) imaging system. We propose a novel Autofocus algorithm, denoted as MLA, that is based on maximum likelihood estimation. MLA belongs to a class of Autofocus algorithms that rely on a known low-return region in the underlying image. We find conditions under which MLA is equivalent to previous methods belonging to the same class. Simulation results show that when compared to previous methods, MLA performs better both in terms of visual quality of the restored image and mean square error (MSE) of the estimated unknown parameters.
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ICASSP - Synthetic Aperture Radar Autofocus via Semidefinite Relaxation
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:Synthetic Aperture Radar (SAR) imaging can suffer from image focus degradation due to unknown platform or target motion. Autofocus algorithms use signal processing techniques to remove the undesired phase errors. The recently proposed multichannel Autofocus models formulate the problem as the solution to Aejφ ≈ 0, where A is a given matrix and φ are the unknown phases. Previous methods approximated ejf using the null vector of A. We propose to approximate ejφ using conic optimization and call this new Autofocus algorithm Semidefinite Relaxation Autofocus (SDRA). Experimental results using a simulated SAR image shows that SDRA has promising performance advantages over existing Autofocus methods.
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Fourier-domain Multichannel Autofocus for synthetic aperture radar
2008 42nd Asilomar Conference on Signals Systems and Computers, 2008Co-Authors: Kuang-hung Liu, David C. MunsonAbstract:We consider the problem of Autofocus for synthetic aperture radar (SAR). We present a new Autofocus algorithm, termed Fourier-domain MultiChannel Autofocus (FMCA), which is derived using a linear algebraic framework that allows the SAR image to be focused in a noniterative fashion. Motivated by the MutiChannel Autofocus (MCA) approach, the proposed Autofocus algorithm also invokes an assumption on the underlying image support. However, it works with the collected Fourier data directly and is capable of accommodating both wide-angle monostatic SAR and bistatic SAR. Previous SAR Autofocus algorithms rely on the assumption that radar's range of look angles is small, and may depend upon characteristics of the SAR scene. The performance of these Autofocus algorithms degrades if the assumptions are violated. The proposed algorithm has the advantage that its formulation makes no assumptions about the range of look angles or characteristics of the scene.
Kuang-hung Liu - One of the best experts on this subject based on the ideXlab platform.
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Synthetic Aperture Radar Autofocus via Semidefinite Relaxation
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2013Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:The Autofocus problem in synthetic aperture radar imaging amounts to estimating unknown phase errors caused by unknown platform or target motion. At the heart of three state-of-the-art Autofocus algorithms, namely, phase gradient Autofocus, multichannel Autofocus (MCA), and Fourier-domain multichannel Autofocus (FMCA), is the solution of a constant modulus quadratic program (CMQP). Currently, these algorithms solve a CMQP by using an eigenvalue relaxation approach. We propose an alternative relaxation approach based on semidefinite programming, which has recently attracted considerable attention in other signal processing problems. Experimental results show that our proposed methods provide promising performance improvements for MCA and FMCA through an increase in computational complexity.
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Fourier-Domain Multichannel Autofocus for Synthetic Aperture Radar
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2011Co-Authors: Kuang-hung Liu, David C. MunsonAbstract:Synthetic aperture radar (SAR) imaging suffers from image focus degradation in the presence of phase errors in the received signal due to unknown platform motion or signal propagation delays. We present a new Autofocus algorithm, termed Fourier-domain multichannel Autofocus (FMCA), that is derived under a linear algebraic framework, allowing the SAR image to be focused in a noniterative fashion. Motivated by the mutichannel Autofocus (MCA) approach, the proposed Autofocus algorithm invokes the assumption of a low-return region, which generally is provided within the antenna sidelobes. Unlike MCA, FMCA works with the collected polar Fourier data directly and is capable of accommodating wide-angle monostatic SAR and bistatic SAR scenarios. Most previous SAR Autofocus algorithms rely on the prior assumption that radar's range of look angles is small so that the phase errors can be modeled as varying along only one dimension in the collected Fourier data. And, in some cases, implicit assumptions are made regarding the SAR scene. Performance of such Autofocus algorithms degrades if the assumptions are not satisfied. The proposed algorithm has the advantage that it does not require prior assumptions about the range of look angles, nor characteristics of the scene.
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ICASSP - Maximum likelihood SAR Autofocus with low-return region
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:Autofocus algorithms deal with image restoration in a nonideal synthetic aperture radar (SAR) imaging system. We propose a novel Autofocus algorithm, denoted as MLA, that is based on maximum likelihood estimation. MLA belongs to a class of Autofocus algorithms that rely on a known low-return region in the underlying image. We find conditions under which MLA is equivalent to previous methods belonging to the same class. Simulation results show that when compared to previous methods, MLA performs better both in terms of visual quality of the restored image and mean square error (MSE) of the estimated unknown parameters.
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ICASSP - Synthetic Aperture Radar Autofocus via Semidefinite Relaxation
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:Synthetic Aperture Radar (SAR) imaging can suffer from image focus degradation due to unknown platform or target motion. Autofocus algorithms use signal processing techniques to remove the undesired phase errors. The recently proposed multichannel Autofocus models formulate the problem as the solution to Aejφ ≈ 0, where A is a given matrix and φ are the unknown phases. Previous methods approximated ejf using the null vector of A. We propose to approximate ejφ using conic optimization and call this new Autofocus algorithm Semidefinite Relaxation Autofocus (SDRA). Experimental results using a simulated SAR image shows that SDRA has promising performance advantages over existing Autofocus methods.
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Fourier-domain Multichannel Autofocus for synthetic aperture radar
2008 42nd Asilomar Conference on Signals Systems and Computers, 2008Co-Authors: Kuang-hung Liu, David C. MunsonAbstract:We consider the problem of Autofocus for synthetic aperture radar (SAR). We present a new Autofocus algorithm, termed Fourier-domain MultiChannel Autofocus (FMCA), which is derived using a linear algebraic framework that allows the SAR image to be focused in a noniterative fashion. Motivated by the MutiChannel Autofocus (MCA) approach, the proposed Autofocus algorithm also invokes an assumption on the underlying image support. However, it works with the collected Fourier data directly and is capable of accommodating both wide-angle monostatic SAR and bistatic SAR. Previous SAR Autofocus algorithms rely on the assumption that radar's range of look angles is small, and may depend upon characteristics of the SAR scene. The performance of these Autofocus algorithms degrades if the assumptions are violated. The proposed algorithm has the advantage that its formulation makes no assumptions about the range of look angles or characteristics of the scene.
Peter Van Beek - One of the best experts on this subject based on the ideXlab platform.
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An Autofocus heuristic for digital cameras based on supervised machine learning
Journal of Heuristics, 2015Co-Authors: Hashim Mir, Rudi Chen, Peter Xu, Peter Van BeekAbstract:Digital cameras are equipped with passive Autofocus mechanisms where a lens is focused using only the camera's optical system and an algorithm for controlling the lens. The speed and accuracy of the Autofocus algorithm are crucial to user satisfaction. In this paper, we address the problems of identifying the global optimum and significant local optima (or peaks) when focusing an image. We show that supervised machine learning techniques can be used to construct a passive Autofocus heuristic for these problems that out-performs an existing hand-crafted heuristic and other baseline methods. In our approach, training and test data were produced using an offline simulation on a suite of 25 benchmarks and correctly labeled in a semi-automated manner. A decision tree learning algorithm was then used to induce an Autofocus heuristic from the data. The automatically constructed machine-learning-based (ml-based) heuristic was compared against a previously proposed hand-crafted heuristic for Autofocusing and other baseline methods. In our experiments, the ml-based heuristic had improved speed--reducing the number of iterations needed to focus by 37.9 % on average in common photography settings and 22.9 % on average in a more difficult focus stacking setting--while maintaining accuracy.
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Improving the accuracy and low-light performance of contrast-based Autofocus using supervised machine learning
Pattern Recognition Letters, 2015Co-Authors: Rudi Chen, Peter Van BeekAbstract:We present a supervised machine learning approach to constructing an Autofocus algorithm.We compare our automatically learned Autofocus algorithm to previously proposed hand-crafted Autofocus algorithms.Our Autofocus algorithm is more accurate, especially in the presence of noise, such as in low-light situations, which are difficult for cameras. The passive Autofocus mechanism is an essential feature of modern digital cameras and needs to be highly accurate to obtain quality images. In this paper, we address the problem of finding a lens position where the image is in focus. We show that supervised machine learning techniques can be used to construct heuristics for a hill-climbing approach for finding such positions that out-performs previously proposed approaches in accuracy and robustly handles scenes with multiple objects at different focus distances and low-light situations. We gather a suite of 32 benchmarks representative of common photography situations and label them in an automated manner. A decision tree learning algorithm is used to induce heuristics from the data and the heuristics are then integrated into a control algorithm. Our experimental evaluation shows improved accuracy over previous work from 91.5% to 98.5% in regular settings and from 70.3% to 94.0% in low-light.
Alberto Moreira - One of the best experts on this subject based on the ideXlab platform.
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an Autofocus approach for residual motion errors with application to airborne repeat pass sar interferometry
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: K A C De Macedo, Rolf Scheiber, Alberto MoreiraAbstract:Airborne repeat-pass SAR systems are very sensible to subwavelength deviations from the reference track. To enable repeat-pass interferometry, a high-precision navigation system is needed. Due to the limit of accuracy of such systems, deviations in the order of centimeters remain between the real track and the processed one, causing mainly undesirable phase undulations and misregistration in the interferograms, referred to as residual motion errors. Up to now, only interferometric approaches, as multisquint, are used to compensate for such residual errors. In this paper, we present for the first time the use of the Autofocus technique for residual motion errors in the repeat-pass interferometric context. A very robust Autofocus technique has to be used to cope with the demands of the repeat-pass applications. We propose a new robust Autofocus algorithm based on the weighted least squares phase estimation and the phase curvature Autofocus (PCA) extended to the range-dependent case. We call this new algorithm weighted PCA. Different from multisquint, the Autofocus approach has the advantage of being able to estimate motion deviations independently, leading to better focused data and correct impulse-response positioning. As a consequence, better coherence and interferometric-phase accuracy are achieved. Repeat-pass interferometry based only on image processing gains in robustness and reliability, since its performance does not deteriorate with time decorrelation and no assumptions need to be made on the interferometric phase. Repeat-pass data of the E-SAR system of the German Aerospace Center (DLR) are used to demonstrate the performance of the proposed approach.
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an Autofocus approach for residual motion errors with application to airborne repeat pass sar interferometry
International Geoscience and Remote Sensing Symposium, 2007Co-Authors: K A C De Macedo, Rolf Scheiber, Alberto MoreiraAbstract:Airborne repeat-pass SAR data are very sensible to sub-wavelength deviations from the reference track. To enable repeat-pass interferometry a high-precision navigation system is needed. Due to the limit of accuracy of such systems, deviations in the order of centimeters remain between the nominal and the processed reference track causing mainly undesirable phase undulations and misregistration in the interferograms, referred as residual motion errors. Up to now only interferometric approaches, as multi-squint, are used to estimate those deviations to compensate for such residuals. In this paper we present for the first time the use of the Autofocus technique for residual motion errors. A very robust Autofocus technique has to be used since the accuracy of the estimated motion has to be at millimeter scale. Because we deal with low-altitude-strip- map mode data we propose a new robust Autofocus technique based on the WLS (Weighted Least-Squares) phase estimation and Phase Curvature Autofocus (PCA) extended to the range- dependent case. We call this new technique WPCA (Weighted PCA). While the multi-squint approach is only able to estimate the baseline variation from coregistered images, the Autofocus approach has the advantage of being able to estimate motion deviations independently for each image. Repeat-pass data of the E-SAR system of the German Aerospace Center (DLR) are used to demonstrate the performance of the proposed approach.
Ami Wiesel - One of the best experts on this subject based on the ideXlab platform.
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Synthetic Aperture Radar Autofocus via Semidefinite Relaxation
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2013Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:The Autofocus problem in synthetic aperture radar imaging amounts to estimating unknown phase errors caused by unknown platform or target motion. At the heart of three state-of-the-art Autofocus algorithms, namely, phase gradient Autofocus, multichannel Autofocus (MCA), and Fourier-domain multichannel Autofocus (FMCA), is the solution of a constant modulus quadratic program (CMQP). Currently, these algorithms solve a CMQP by using an eigenvalue relaxation approach. We propose an alternative relaxation approach based on semidefinite programming, which has recently attracted considerable attention in other signal processing problems. Experimental results show that our proposed methods provide promising performance improvements for MCA and FMCA through an increase in computational complexity.
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ICASSP - Maximum likelihood SAR Autofocus with low-return region
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:Autofocus algorithms deal with image restoration in a nonideal synthetic aperture radar (SAR) imaging system. We propose a novel Autofocus algorithm, denoted as MLA, that is based on maximum likelihood estimation. MLA belongs to a class of Autofocus algorithms that rely on a known low-return region in the underlying image. We find conditions under which MLA is equivalent to previous methods belonging to the same class. Simulation results show that when compared to previous methods, MLA performs better both in terms of visual quality of the restored image and mean square error (MSE) of the estimated unknown parameters.
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ICASSP - Synthetic Aperture Radar Autofocus via Semidefinite Relaxation
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Kuang-hung Liu, Ami Wiesel, David C. MunsonAbstract:Synthetic Aperture Radar (SAR) imaging can suffer from image focus degradation due to unknown platform or target motion. Autofocus algorithms use signal processing techniques to remove the undesired phase errors. The recently proposed multichannel Autofocus models formulate the problem as the solution to Aejφ ≈ 0, where A is a given matrix and φ are the unknown phases. Previous methods approximated ejf using the null vector of A. We propose to approximate ejφ using conic optimization and call this new Autofocus algorithm Semidefinite Relaxation Autofocus (SDRA). Experimental results using a simulated SAR image shows that SDRA has promising performance advantages over existing Autofocus methods.
