Results 241 to 250 of about 1,508,834 (285)
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2010
In atmospheric remote sensing, near real-time software processors frequently use approximations of the Jacobian matrix in order to speed up the calculation. If the forward model F(x) depends on the state vector x through some model parameters bk, F(x) = F(b1 (x),..., bN (x)),
Adrian Doicu +2 more
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In atmospheric remote sensing, near real-time software processors frequently use approximations of the Jacobian matrix in order to speed up the calculation. If the forward model F(x) depends on the state vector x through some model parameters bk, F(x) = F(b1 (x),..., bN (x)),
Adrian Doicu +2 more
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Image denoising using total least squares
IEEE Transactions on Image Processing, 2006In this paper, we present a method for removing noise from digital images corrupted with additive, multiplicative, and mixed noise. An image patch from an ideal image is modeled as a linear combination of image patches from the noisy image. We propose to fit this model to the real-world image data in the total least square (TLS) sense, because the TLS ...
Keigo, Hirakawa, Thomas W, Parks
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On the equivalence of constrained total least squares and structured total least squares
IEEE Transactions on Signal Processing, 1996Several extensions of the total least squares (TLS) method that are able to calculate a structured rank deficient approximation of a data matrix have been developed. The main result of this article is the demonstration of the equivalence of two of these approaches, namely, the constrained total least squares (CTLS) approach and the structured total ...
P. Lemmerling, B. De Moor, S. Van Huffel
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2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628), 2003
In the robot navigation problem, noisy sensor data. must be filtered to obtain the best estimate of the robot position. The discrete Kalman filter, which usually is used for prediction and detection of signals in communication and control problems has become a commonly used method to reduce the effect of uncertainty from the sensor data.
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In the robot navigation problem, noisy sensor data. must be filtered to obtain the best estimate of the robot position. The discrete Kalman filter, which usually is used for prediction and detection of signals in communication and control problems has become a commonly used method to reduce the effect of uncertainty from the sensor data.
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Structured Total Least Squares
2002In this paper an overview is given of the Structured Total Least Squares (STLS) approach and its recent extensions. The Structured Total Least Squares (STLS) problem is a natural extension of the Total Least Squares (TLS) problem when constraints on the matrix structure need to be imposed.
Philippe Lemmerling, Sabine Van Huffel
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2018
The chapter treats total least squares (TLS), which in statistics corresponds to orthogonal regression. Some different extensions are discussed, including ways to show how uncertainties in different matrix elements may be related or correlated. The application of TLS to identification of dynamic systems is also treated.
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The chapter treats total least squares (TLS), which in statistics corresponds to orthogonal regression. Some different extensions are discussed, including ways to show how uncertainties in different matrix elements may be related or correlated. The application of TLS to identification of dynamic systems is also treated.
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On the weighting method for mixed least squares–total least squares problems
Numerical Linear Algebra with Applications, 2017SummaryIt is well known that the standard algorithm for the mixed least squares–total least squares (MTLS) problem uses the QR factorization to reduce the original problem into a standard total least squares problem with smaller size, which can be solved based on the singular value decomposition (SVD).
Qiaohua Liu, Minghui Wang
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Total least square kernel regression
Journal of Visual Communication and Image Representation, 2012In this paper, we study the problem of robust image fusion in the context of multi-frame super-resolution. Given multiple aligned noisy low-resolution images, image fusion produces a new image on a high-resolution grid. Recently, kernel regression is presented as a powerful image fusion technique.
Hiêp Luong +3 more
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Total least mean squares algorithm
IEEE Transactions on Signal Processing, 1998Widrow (1971) proposed the least mean squares (LMS) algorithm, which has been extensively applied in adaptive signal processing and adaptive control. The LMS algorithm is based on the minimum mean squares error. On the basis of the total least mean squares error or the minimum Raleigh quotient, we propose the total least mean squares (TLMS) algorithm ...
null Da-Zheng Feng +2 more
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Least squares and total least squares methods in image restoration
1997Image restoration is the process of removing or minimizing degradations (blur) in an image. Mathematically, it can be modeled as a discrete ill-posed problem Hf=g, where H is a matrix of large dimension representing the blurring phenomena, and g is a vector representing the observed image.
Julie Kamm, James G. Nagy
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