Filters








11,364 Hits in 4.4 sec

Adaptive sampling and reconstruction using greedy error minimization

Fabrice Rousselle, Claude Knaus, Matthias Zwicker
2011 Proceedings of the 2011 SIGGRAPH Asia Conference on - SA '11  
Figure 1 : We minimize MSE in Monte Carlo rendering by adaptive sampling and reconstruction in image space.  ...  Abstract We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering.  ...  Mihovil Odak, gargoyle courtesy of INRIA via the AIM@SHAPE repository, plants ecosystem from Deussen et al. [1998] , and yeahright by Keenan Crane.  ... 
doi:10.1145/2024156.2024193 fatcat:rvjwsbvtvvfmpfete7ahi37cnu

Adaptive sampling and reconstruction using greedy error minimization

Fabrice Rousselle, Claude Knaus, Matthias Zwicker
2011 Proceedings of the 2011 SIGGRAPH Asia Conference on - SA '11  
Figure 1 : We minimize MSE in Monte Carlo rendering by adaptive sampling and reconstruction in image space.  ...  Abstract We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering.  ...  Mihovil Odak, gargoyle courtesy of INRIA via the AIM@SHAPE repository, plants ecosystem from Deussen et al. [1998] , and yeahright by Keenan Crane.  ... 
doi:10.1145/2070752.2024193 fatcat:zwytozflfzfdxnpbhdkjbkf4xm

Adaptive sampling and reconstruction using greedy error minimization

Fabrice Rousselle, Claude Knaus, Matthias Zwicker
2011 ACM Transactions on Graphics  
Figure 1 : We minimize MSE in Monte Carlo rendering by adaptive sampling and reconstruction in image space.  ...  Abstract We introduce a novel approach for image space adaptive sampling and reconstruction in Monte Carlo rendering.  ...  Mihovil Odak, gargoyle courtesy of INRIA via the AIM@SHAPE repository, plants ecosystem from Deussen et al. [1998] , and yeahright by Keenan Crane.  ... 
doi:10.1145/2070781.2024193 fatcat:5ocaydfxr5hwdkuxu74cxqmsoa

Time-Stampless Adaptive Nonuniform Sampling for Stochastic Signals

Soheil Feizi, Vivek K Goyal, Muriel Medard
2012 IEEE Transactions on Signal Processing  
Since only past samples are used in computing time increments, it is not necessary to save sampling times (time stamps) for use in the reconstruction process.  ...  In this paper, we introduce a time-stampless adaptive nonuniform sampling (TANS) framework, in which time increments between samples are determined by a function of the m most recent increments and sample  ...  In a practical setting involving both sampling and quantization, backward adaptivity requires using the quantized values to drive the adaptation [5] .  ... 
doi:10.1109/tsp.2012.2208633 fatcat:hev54rpsgjbpxac7i3ihkahluy

Time-stampless adaptive nonuniform sampling for stochastic signals

Soheil Feizi, Vivek K Goyal, Muriel Medard
2012 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Since only past samples are used in computing time increments, it is not necessary to save sampling times (time stamps) for use in the reconstruction process.  ...  In this paper, we introduce a time-stampless adaptive nonuniform sampling (TANS) framework, in which time increments between samples are determined by a function of the m most recent increments and sample  ...  In a practical setting involving both sampling and quantization, backward adaptivity requires using the quantized values to drive the adaptation [5] .  ... 
doi:10.1109/icassp.2012.6288747 dblp:conf/icassp/FeiziGM12 fatcat:k62czdccerb77fmkclnpdbjcv4

Comparison of some commonly used algorithms for sparse signal reconstruction [article]

Milan Resetar, Gojko Ratkovic, Svetlana Zecevic
2019 arXiv   pre-print
In this paper some of the commonly used algorithms for sparse signal recovery are compared. The reconstruction accuracy, mean squared error and the execution time are compared.  ...  This has triggered scientists to examine the possibilities of creating a new path for recovering signals using much less samples and therefore speeding up the process and satisfying the need for faster  ...  Least Squares algorithm, Adaptive Gradient based Algorithm [16] , [17] and L1-Minimization Algorithm With Equality Constraints.  ... 
arXiv:1902.07309v1 fatcat:fktq2jtp35bkvnngc657uoveme

A Modified Adaptive Sparse Channel Estimator for OFDM Systems Based On Singular Value Decomposition

Liu Zhiyong, Wang Yirong, Liu Weicai
2016 International Journal of Future Generation Communication and Networking  
The proposed channel estimation has better robustness to noise and low error.  ...  Because using the SVD to modify the measurement matrix of CS can improve the robustness to noise. So we use the SVD to modify the measurement matrix of ASMP based compressive channel estimation.  ...  However, the l0-norm minimization is transformative to l1-norm minimization 1 2 min . . p h h s t Y Ah   (3) To solve the convex program, we often used reconstruction algorithm which is a very important  ... 
doi:10.14257/ijfgcn.2016.9.11.16 fatcat:r5as4x7ejzbvzhgx6hx5bhukra

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction

Michael M. Abdel-Sayed, Ahmed Khattab, Mohamed F. Abu-Elyazeed
2016 Journal of Advanced Research  
It is even superior to ' 1 minimization in terms of the normalized time-error product, a new metric introduced to measure the tradeoff between the reconstruction time and error.  ...  time and error.  ...  In this case, the measured vector is given by y ¼ Ux þ e; ð5Þ where e is the sample noise and kek 2 < . ' 1 minimization can still be used to reconstruct the original sparse signal x with an error that  ... 
doi:10.1016/j.jare.2016.08.005 pmid:27672448 pmcid:PMC5030340 fatcat:xhcqp4hhbvfmth3c5tuarub4pm

An adaptive greedy algorithm with application to sparse NARMA identification

Gerasimos Mileounis, Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements.  ...  In this paper a powerful greedy scheme developed in [1, 2] is converted into an adaptive algorithm which is applied to estimation of nonlinear channels.  ...  The basic principle behind greedy algorithms is to iteratively find the support set of the sparse vector and reconstruct the signal using the restricted support Least Squares (LS) estimate.  ... 
doi:10.1109/icassp.2010.5495838 fatcat:woggtoo3rbfa7gnx6krpgdilkm

Methods for Quantized Compressed Sensing [article]

Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu, Shenyinying Tu, Deanna Needell
2015 arXiv   pre-print
We also introduce two new greedy approaches for reconstruction: Quantized Compressed Sampling Matching Pursuit (QCoSaMP) and Adaptive Outlier Pursuit for Quantized Iterative Hard Thresholding (AOP-QIHT  ...  In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements.  ...  We study several greedy approaches to solving this problem, and catalog precisely the tradeoff between reconstruction error, bit depth, and number of measurements.  ... 
arXiv:1512.09184v1 fatcat:3ipczqs2g5fwfcftf33bg2ikwe

Methods for quantized compressed sensing

Hao-Jun Michael Shi, Mindy Case, Xiaoyi Gu, Shenyinying Tu, Deanna Needell
2016 2016 Information Theory and Applications Workshop (ITA)  
We also introduce two new greedy approaches for reconstruction: Quantized Compressed Sampling Matching Pursuit (QCoSaMP) and Adaptive Outlier Pursuit for Quantized Iterative Hard Thresholding (AOP-QIHT  ...  In this paper, we compare and catalog the performance of various greedy quantized compressed sensing algorithms that reconstruct sparse signals from quantized compressed measurements.  ...  We study several greedy approaches to solving this problem, and catalog precisely the tradeoff between reconstruction error, bit depth, and number of measurements.  ... 
doi:10.1109/ita.2016.7888203 dblp:conf/ita/ShiCGTN16 fatcat:nn3nzdsb3bdejfrqc2i7a3qgbm

Sparse Signals Reconstruction via Adaptive Iterative Greedy Algorithm

Walid Osamy, Ahmed Salim, Ahmed Aziz
2014 International Journal of Computer Applications  
In order to improve CS reconstruction performance, this paper present a novel reconstruction greedy algorithm called the Enhanced Orthogonal Matching Pursuit (E-OMP).  ...  Furthermore, E-OMP uses a simple backtracking step to detect the previous chosen columns accuracy and then remove the false columns at each time.  ...  Fig.5a and Fig.5b depicts the reconstruction error for the noisy binary and uniform sparse signals. In this test E-OMP clearly produces less error than ROPM and OMP.  ... 
doi:10.5120/15810-4715 fatcat:7qj6haxoxjhq3alsijwdza7nam

A Framework for ODF Inference by Using Fiber Tract Adaptive MPG Selection [chapter]

Hidekata Hontani, Kazunari Iwamoto, Yoshitaka Masutani
2013 Mathematics and Visualization  
Given a training set of diffusion magnetic resonance (MR) images, the method selects the set of MPG directions by minimizing a cost function, which represents the square errors of the reconstructed oriented  ...  Experimental results demonstrated that the set of MPG directions selected by our proposed method reconstructed the ODFs more accurately than an existing method based on spherical harmonics and on greedy  ...  The greedy algorithm shown in the previous section is used for minimizing E DWS (Ω m ).  ... 
doi:10.1007/978-3-319-02475-2_7 fatcat:7uvryujhzrbjho64afus6zpbby

An Adaptive Method for Choosing Center Sets of RBF Interpolation

Dianxuan Gong, Jincai Chang, Chuanan Wei
2011 Journal of Computers  
In this paper, we give a short overview of these algorithms including thinning algorithm, greedy algorithm, arclength equipartition like algorithm and k-means clustering algorithm.  ...  A new adaptive data-dependent method is provided at the end with some numerical examples to show its effectiveness..  ...  Obviously, greedy algorithm is data-dependent and adaptive algorithm. Greedy Algorithm. (1) Let { } 1 1 X x = for 1 x ∈ Ω arbitrary.  ... 
doi:10.4304/jcp.6.10.2112-2119 fatcat:q47ysa3rynca7pxktitnivesx4

SURE-based optimization for adaptive sampling and reconstruction

Tzu-Mao Li, Yu-Ting Wu, Yung-Yu Chuang
2012 ACM Transactions on Graphics  
Figure 1 : Comparisons between greedy error minimization (GEM) [Rousselle et al. 2011 ] and our SURE-based filtering.  ...  It also allows us to allocate more samples to areas with higher estimated MSE. Adaptive sampling and reconstruction can therefore be processed within an optimization framework.  ...  We would like to thank the anonymous reviewers for their valuable comments and the creators of the models used in this paper: airplane (Pedro Caparros), Ferrari (Render-Here), buildings, street cones (  ... 
doi:10.1145/2366145.2366213 fatcat:poivggmmrjgenbw7urxbu77mvu
« Previous Showing results 1 — 15 out of 11,364 results