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ICASSP 2018

ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2018 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics. Visit ICASSP 2018.

MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER

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Authors:
Yang Li, Qian He, Rick Blum
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19 April 2018 - 10:51pm
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MIMO RADAR, DETECTION,TRANSMITTER SELECTION

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[1] Yang Li, Qian He, Rick Blum, "MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3058. Accessed: Apr. 21, 2018.
@article{3058-18,
url = {http://sigport.org/3058},
author = {Yang Li; Qian He; Rick Blum },
publisher = {IEEE SigPort},
title = {MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER},
year = {2018} }
TY - EJOUR
T1 - MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER
AU - Yang Li; Qian He; Rick Blum
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3058
ER -
Yang Li, Qian He, Rick Blum. (2018). MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER. IEEE SigPort. http://sigport.org/3058
Yang Li, Qian He, Rick Blum, 2018. MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER. Available at: http://sigport.org/3058.
Yang Li, Qian He, Rick Blum. (2018). "MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER." Web.
1. Yang Li, Qian He, Rick Blum. MIMO RADAR TARGET DETECTION USING LOW-COMPLEXITY RECEIVER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3058

Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution


Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation for HSR, which is a non-convex large-scale optimization problem. Our contributions contain both computational and theoretical aspects.

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Authors:
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu
Submitted On:
19 April 2018 - 10:39pm
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ICASSP 2018 modified.pdf

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[1] Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu, "Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3057. Accessed: Apr. 21, 2018.
@article{3057-18,
url = {http://sigport.org/3057},
author = {Ruiyuan Wu; Chun-Hei Chan; Hoi-To Wai; Wing-Kin Ma; and Xiao Fu },
publisher = {IEEE SigPort},
title = {Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution},
year = {2018} }
TY - EJOUR
T1 - Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution
AU - Ruiyuan Wu; Chun-Hei Chan; Hoi-To Wai; Wing-Kin Ma; and Xiao Fu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3057
ER -
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. (2018). Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution. IEEE SigPort. http://sigport.org/3057
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu, 2018. Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution. Available at: http://sigport.org/3057.
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. (2018). "Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution." Web.
1. Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3057

MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING


In this paper, we design a novel deep learning based hybrid system for automatic chord recognition. Currently, there is a bottleneck in the amount of enough annotated data for training robust acoustic models, as hand annotating time-synchronized chord labels requires professional musical skills and considerable labor. As a solution to this problem, we construct a large set of time synchronized MIDI-audio pairs, and use these data to train a Deep Residual Network (DRN) feature extractor, which can then estimate pitch class activations of real-world music audio recordings.

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19 April 2018 - 10:25pm
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ICASSP2018Poster_WuYiming.pdf

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[1] , "MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3056. Accessed: Apr. 21, 2018.
@article{3056-18,
url = {http://sigport.org/3056},
author = { },
publisher = {IEEE SigPort},
title = {MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING},
year = {2018} }
TY - EJOUR
T1 - MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3056
ER -
. (2018). MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING. IEEE SigPort. http://sigport.org/3056
, 2018. MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING. Available at: http://sigport.org/3056.
. (2018). "MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING." Web.
1. . MUSIC CHORD RECOGNITION BASED ON MIDI-TRAINED DEEP FEATURE AND BLSTM-CRF HYBIRD DECODING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3056

ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION


In certain applications such as zero-resource speech processing
or very-low resource speech-language systems, it might
not be feasible to collect speech activity detection (SAD) annotations.
However, the state-of-the-art supervised SAD techniques
based on neural networks or other machine learning
methods require annotated training data matched to the target
domain. This paper establish a clustering approach for fully
unsupervised SAD useful for cases where SAD annotations
are not available. The proposed approach leverages Hartigan

Paper Details

Authors:
Harishchandra Dubey, Abhijeet Sangwan, John H. L. Hansen+
Submitted On:
19 April 2018 - 10:02pm
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FINAL_April13_7.ICASSP-Poster-2018-DipSAD_v2.pdf

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[1] Harishchandra Dubey, Abhijeet Sangwan, John H. L. Hansen+, "ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3055. Accessed: Apr. 21, 2018.
@article{3055-18,
url = {http://sigport.org/3055},
author = {Harishchandra Dubey; Abhijeet Sangwan; John H. L. Hansen+ },
publisher = {IEEE SigPort},
title = {ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION},
year = {2018} }
TY - EJOUR
T1 - ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION
AU - Harishchandra Dubey; Abhijeet Sangwan; John H. L. Hansen+
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3055
ER -
Harishchandra Dubey, Abhijeet Sangwan, John H. L. Hansen+. (2018). ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION. IEEE SigPort. http://sigport.org/3055
Harishchandra Dubey, Abhijeet Sangwan, John H. L. Hansen+, 2018. ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION. Available at: http://sigport.org/3055.
Harishchandra Dubey, Abhijeet Sangwan, John H. L. Hansen+. (2018). "ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION." Web.
1. Harishchandra Dubey, Abhijeet Sangwan, John H. L. Hansen+. ROBUST FEATURE CLUSTERING FOR UNSUPERVISED SPEECH ACTIVITY DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3055

HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING

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Authors:
Asa Oines, Eugene Weinstein, Pedro Moreno
Submitted On:
19 April 2018 - 9:47pm
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FLMN Poster.pdf

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[1] Asa Oines, Eugene Weinstein, Pedro Moreno, "HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3054. Accessed: Apr. 21, 2018.
@article{3054-18,
url = {http://sigport.org/3054},
author = {Asa Oines; Eugene Weinstein; Pedro Moreno },
publisher = {IEEE SigPort},
title = {HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING},
year = {2018} }
TY - EJOUR
T1 - HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING
AU - Asa Oines; Eugene Weinstein; Pedro Moreno
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3054
ER -
Asa Oines, Eugene Weinstein, Pedro Moreno. (2018). HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING. IEEE SigPort. http://sigport.org/3054
Asa Oines, Eugene Weinstein, Pedro Moreno, 2018. HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING. Available at: http://sigport.org/3054.
Asa Oines, Eugene Weinstein, Pedro Moreno. (2018). "HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING." Web.
1. Asa Oines, Eugene Weinstein, Pedro Moreno. HYBRID LSTM-FSMN NETWORKS FOR ACOUSTIC MODELING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3054

COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION


Deep Neural Network (DNN) is a basic method used for the rare Acoustic Event Detection (AED) in synthesised audio. The structure of DNNs including Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN) for AED tasks has rather fewer hidden layers compared with computer vision systems. This paper tries to demonstrate that a DNN with more hidden layers does not necessarily guarantee a better performance in AED tasks.

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Authors:
Shengchen Li
Submitted On:
19 April 2018 - 9:58pm
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COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION.pdf

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[1] Shengchen Li, "COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3053. Accessed: Apr. 21, 2018.
@article{3053-18,
url = {http://sigport.org/3053},
author = {Shengchen Li },
publisher = {IEEE SigPort},
title = {COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION},
year = {2018} }
TY - EJOUR
T1 - COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION
AU - Shengchen Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3053
ER -
Shengchen Li. (2018). COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION. IEEE SigPort. http://sigport.org/3053
Shengchen Li, 2018. COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION. Available at: http://sigport.org/3053.
Shengchen Li. (2018). "COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION." Web.
1. Shengchen Li. COMPARING THE INFLUENCE OF DEPTH AND WIDTH OF DEEP NEURAL NETWORK BASED ON FIXED NUMBER OF PARAMETERS FOR AUDIO EVENT DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3053

L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING


Single-image blind deblurring is a challenging ill-posed in- verse problem which aims to estimate both blur kernel and latent sharp image from only one observation. This paper fo- cuses on first estimating the blur kernel alone and then restor- ing the latent image since it has been proven to be more feasi- ble to handle the ill-posed nature during blind deblurring. To estimate an accurate blur kernel, L0-norm of both first- and second-order image gradients is proposed to regularize the final estimation result.

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Authors:
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng
Submitted On:
19 April 2018 - 9:44pm
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ICASSP2018-LECTURE .pdf

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[1] Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng, "L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3052. Accessed: Apr. 21, 2018.
@article{3052-18,
url = {http://sigport.org/3052},
author = {Ryan Wen Liu; Wei Yin; Shengwu Xiong; Silong Peng },
publisher = {IEEE SigPort},
title = {L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING },
year = {2018} }
TY - EJOUR
T1 - L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING
AU - Ryan Wen Liu; Wei Yin; Shengwu Xiong; Silong Peng
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3052
ER -
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng. (2018). L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING . IEEE SigPort. http://sigport.org/3052
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng, 2018. L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING . Available at: http://sigport.org/3052.
Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng. (2018). "L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING ." Web.
1. Ryan Wen Liu, Wei Yin, Shengwu Xiong, Silong Peng. L0-REGULARIZED HYBRID GRADIENT SPARSITY PRIORS FOR ROBUST SINGLE-IMAGE BLIND DEBLURRING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3052

Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents

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Authors:
Haiquan Zhao, Rodrigo C. de Lamare, and Yuriy Zakharov
Submitted On:
19 April 2018 - 9:42pm
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presentation file for ICASSP 2018.pdf

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[1] Haiquan Zhao, Rodrigo C. de Lamare, and Yuriy Zakharov, "Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3051. Accessed: Apr. 21, 2018.
@article{3051-18,
url = {http://sigport.org/3051},
author = {Haiquan Zhao; Rodrigo C. de Lamare; and Yuriy Zakharov },
publisher = {IEEE SigPort},
title = {Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents},
year = {2018} }
TY - EJOUR
T1 - Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents
AU - Haiquan Zhao; Rodrigo C. de Lamare; and Yuriy Zakharov
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3051
ER -
Haiquan Zhao, Rodrigo C. de Lamare, and Yuriy Zakharov. (2018). Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents. IEEE SigPort. http://sigport.org/3051
Haiquan Zhao, Rodrigo C. de Lamare, and Yuriy Zakharov, 2018. Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents. Available at: http://sigport.org/3051.
Haiquan Zhao, Rodrigo C. de Lamare, and Yuriy Zakharov. (2018). "Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents." Web.
1. Haiquan Zhao, Rodrigo C. de Lamare, and Yuriy Zakharov. Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3051

A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT

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Submitted On:
19 April 2018 - 9:35pm
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ICASSP2018_poster.pdf

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[1] , "A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3050. Accessed: Apr. 21, 2018.
@article{3050-18,
url = {http://sigport.org/3050},
author = { },
publisher = {IEEE SigPort},
title = {A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT},
year = {2018} }
TY - EJOUR
T1 - A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3050
ER -
. (2018). A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT. IEEE SigPort. http://sigport.org/3050
, 2018. A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT. Available at: http://sigport.org/3050.
. (2018). "A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT." Web.
1. . A DEEP NEURAL NETWORK BASED METHOD OF SOURCE LOCALIZATION IN A SHALLOWWATER ENVIRONMENT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3050

Analysis and Optimization of Aperture Design in Computational Imaging


There is growing interest in the use of coded aperture imaging systems for a variety of applications. Using an analysis framework based on mutual information, we examine the fundamental limits of such systems—and the associated optimum aperture coding—under simple but meaningful propagation and sensor models. Among other results, we show that when SNR is high and thermal noise dominates shot noise, spectrally-flat masks, which have 50% transmissivity, are optimal, but that when shot noise dominates thermal noise, randomly generated masks with lower transmissivity offer greater performance.

Paper Details

Authors:
Adam Yedidia, Christos Thrampoulidis, Gregory Wornell
Submitted On:
19 April 2018 - 9:20pm
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icassp_talk_short.pptx

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[1] Adam Yedidia, Christos Thrampoulidis, Gregory Wornell, "Analysis and Optimization of Aperture Design in Computational Imaging", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3049. Accessed: Apr. 21, 2018.
@article{3049-18,
url = {http://sigport.org/3049},
author = {Adam Yedidia; Christos Thrampoulidis; Gregory Wornell },
publisher = {IEEE SigPort},
title = {Analysis and Optimization of Aperture Design in Computational Imaging},
year = {2018} }
TY - EJOUR
T1 - Analysis and Optimization of Aperture Design in Computational Imaging
AU - Adam Yedidia; Christos Thrampoulidis; Gregory Wornell
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3049
ER -
Adam Yedidia, Christos Thrampoulidis, Gregory Wornell. (2018). Analysis and Optimization of Aperture Design in Computational Imaging. IEEE SigPort. http://sigport.org/3049
Adam Yedidia, Christos Thrampoulidis, Gregory Wornell, 2018. Analysis and Optimization of Aperture Design in Computational Imaging. Available at: http://sigport.org/3049.
Adam Yedidia, Christos Thrampoulidis, Gregory Wornell. (2018). "Analysis and Optimization of Aperture Design in Computational Imaging." Web.
1. Adam Yedidia, Christos Thrampoulidis, Gregory Wornell. Analysis and Optimization of Aperture Design in Computational Imaging [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3049

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