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Image/Video Storage, Retrieval

Model Corrected Low Rank Ptychography


In this paper, we introduce a novel algorithmic framework for sub-diffractive super-resolution imaging of dynamic, time varying targets. We extend recent works in low rank Fourier ptychographic imaging, to incorporate model-correction schemes, which correct for errors propagated due to inaccuracies in fitting an exact low rank model to the target video acquired. Through our algorithm, we are able to demonstrate superior reconstruction quality of video from phaseless Fourier ptychographic measurements, at low sample complexities, as compared to conventional ptychographic setups.

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Authors:
Chinmay Hegde, Namrata Vaswani
Submitted On:
8 October 2018 - 2:21pm
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poster_icip.pdf

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[1] Chinmay Hegde, Namrata Vaswani, "Model Corrected Low Rank Ptychography", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3628. Accessed: Nov. 13, 2018.
@article{3628-18,
url = {http://sigport.org/3628},
author = {Chinmay Hegde; Namrata Vaswani },
publisher = {IEEE SigPort},
title = {Model Corrected Low Rank Ptychography},
year = {2018} }
TY - EJOUR
T1 - Model Corrected Low Rank Ptychography
AU - Chinmay Hegde; Namrata Vaswani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3628
ER -
Chinmay Hegde, Namrata Vaswani. (2018). Model Corrected Low Rank Ptychography. IEEE SigPort. http://sigport.org/3628
Chinmay Hegde, Namrata Vaswani, 2018. Model Corrected Low Rank Ptychography. Available at: http://sigport.org/3628.
Chinmay Hegde, Namrata Vaswani. (2018). "Model Corrected Low Rank Ptychography." Web.
1. Chinmay Hegde, Namrata Vaswani. Model Corrected Low Rank Ptychography [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3628

PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL


We propose a novel method for content-based image retrieval based on the features extracted from the convolutional layers of the deep neural network architecture. Some of the popular approaches form the feature vectors from the fully connected layers of the convolutional neural networks or directly concatenate the features from the convolutional layers. However, the main problem with the use of feature vectors from fully connected layers is that the spatial information about the objects is lost. This motivated us to use the features from the convolutional layer.

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Authors:
Abin Jose, Ricard Durall Lopez, Iris Heisterklaus, Mathias Wien
Submitted On:
6 October 2018 - 3:41am
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ICIP-Presentation.pdf

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[1] Abin Jose, Ricard Durall Lopez, Iris Heisterklaus, Mathias Wien, "PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3568. Accessed: Nov. 13, 2018.
@article{3568-18,
url = {http://sigport.org/3568},
author = {Abin Jose; Ricard Durall Lopez; Iris Heisterklaus; Mathias Wien },
publisher = {IEEE SigPort},
title = {PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL},
year = {2018} }
TY - EJOUR
T1 - PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL
AU - Abin Jose; Ricard Durall Lopez; Iris Heisterklaus; Mathias Wien
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3568
ER -
Abin Jose, Ricard Durall Lopez, Iris Heisterklaus, Mathias Wien. (2018). PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL. IEEE SigPort. http://sigport.org/3568
Abin Jose, Ricard Durall Lopez, Iris Heisterklaus, Mathias Wien, 2018. PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL. Available at: http://sigport.org/3568.
Abin Jose, Ricard Durall Lopez, Iris Heisterklaus, Mathias Wien. (2018). "PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL." Web.
1. Abin Jose, Ricard Durall Lopez, Iris Heisterklaus, Mathias Wien. PYRAMID POOLING OF CONVOLTIONAL FEATURE MAPS FOR IMAGE RETRIEVAL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3568

HADAMARD CODED DISCRETE CROSS MODAL HASHING


Cross-modal retrieval is a hot topic in the fields of machine learning and media retrieval, making it possible to relate different types of media, such as image, text, and audio. A powerful method for the cross-modal retrieval, discrete cross-modal hashing (DCH), has recently been proposed. The DCH can encode different types of media feature vectors to binary codes. When stored in a database, the binary code makes searches efficient because the Hamming distance between the corresponding sections of two binary codes can be computed via a specialized CPU operation.

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Authors:
Koichi Eto, Gou Koutaki, Keiichiro Shirai
Submitted On:
6 October 2018 - 3:36am
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ICIPposter.pdf

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[1] Koichi Eto, Gou Koutaki, Keiichiro Shirai, "HADAMARD CODED DISCRETE CROSS MODAL HASHING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3567. Accessed: Nov. 13, 2018.
@article{3567-18,
url = {http://sigport.org/3567},
author = {Koichi Eto; Gou Koutaki; Keiichiro Shirai },
publisher = {IEEE SigPort},
title = {HADAMARD CODED DISCRETE CROSS MODAL HASHING},
year = {2018} }
TY - EJOUR
T1 - HADAMARD CODED DISCRETE CROSS MODAL HASHING
AU - Koichi Eto; Gou Koutaki; Keiichiro Shirai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3567
ER -
Koichi Eto, Gou Koutaki, Keiichiro Shirai. (2018). HADAMARD CODED DISCRETE CROSS MODAL HASHING. IEEE SigPort. http://sigport.org/3567
Koichi Eto, Gou Koutaki, Keiichiro Shirai, 2018. HADAMARD CODED DISCRETE CROSS MODAL HASHING. Available at: http://sigport.org/3567.
Koichi Eto, Gou Koutaki, Keiichiro Shirai. (2018). "HADAMARD CODED DISCRETE CROSS MODAL HASHING." Web.
1. Koichi Eto, Gou Koutaki, Keiichiro Shirai. HADAMARD CODED DISCRETE CROSS MODAL HASHING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3567

DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION


poster.pdf

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Authors:
Sihui Luo, Yezhou Yang, Mingli Song
Submitted On:
4 October 2018 - 10:24pm
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poster.pdf

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[1] Sihui Luo, Yezhou Yang, Mingli Song, "DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3478. Accessed: Nov. 13, 2018.
@article{3478-18,
url = {http://sigport.org/3478},
author = {Sihui Luo; Yezhou Yang; Mingli Song },
publisher = {IEEE SigPort},
title = {DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION},
year = {2018} }
TY - EJOUR
T1 - DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION
AU - Sihui Luo; Yezhou Yang; Mingli Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3478
ER -
Sihui Luo, Yezhou Yang, Mingli Song. (2018). DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION. IEEE SigPort. http://sigport.org/3478
Sihui Luo, Yezhou Yang, Mingli Song, 2018. DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION. Available at: http://sigport.org/3478.
Sihui Luo, Yezhou Yang, Mingli Song. (2018). "DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION." Web.
1. Sihui Luo, Yezhou Yang, Mingli Song. DEEPSSH: DEEP SEMANTIC STRUCTURED HASHING FOR EXPLAINABLE PERSON RE-IDENTIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3478

WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL


Spatial pooling over convolutional activations (e.g., max pooling or sum pooling) has been shown to be successful in learning deep representations for image retrieval. However, most pooling techniques assume that every activation is equally important, and as a result they suffer from the presence of uninformative image regions that play a negative role as regards matching or lead to the confusion of particular visual instances. To address this issue, we propose a trainable building block that steers pooling to local information important to the task at hand.

TP.L2.4.pdf

PDF icon TP.L2.4.pdf (19 downloads)

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Authors:
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino
Submitted On:
4 October 2018 - 10:08pm
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TP.L2.4.pdf

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[1] Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino, "WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3477. Accessed: Nov. 13, 2018.
@article{3477-18,
url = {http://sigport.org/3477},
author = {Xiaomeng Wu; Go Irie; Kaoru Hiramatsu; and Kunio Kashino },
publisher = {IEEE SigPort},
title = {WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL},
year = {2018} }
TY - EJOUR
T1 - WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL
AU - Xiaomeng Wu; Go Irie; Kaoru Hiramatsu; and Kunio Kashino
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3477
ER -
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. (2018). WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL. IEEE SigPort. http://sigport.org/3477
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino, 2018. WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL. Available at: http://sigport.org/3477.
Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. (2018). "WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL." Web.
1. Xiaomeng Wu, Go Irie, Kaoru Hiramatsu, and Kunio Kashino. WEIGHTED GENERALIZED MEAN POOLING FOR DEEP IMAGE RETRIEVAL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3477

SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION

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Authors:
Shagan Sah, Dheeraj Peri, AmeyaShringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Raymond Ptucha
Submitted On:
4 October 2018 - 11:26am
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ICIP_2018_Poster_2472.pdf

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[1] Shagan Sah, Dheeraj Peri, AmeyaShringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Raymond Ptucha, "SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3437. Accessed: Nov. 13, 2018.
@article{3437-18,
url = {http://sigport.org/3437},
author = {Shagan Sah; Dheeraj Peri; AmeyaShringi; Chi Zhang; Miguel Dominguez; Andreas Savakis; Raymond Ptucha },
publisher = {IEEE SigPort},
title = {SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION},
year = {2018} }
TY - EJOUR
T1 - SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION
AU - Shagan Sah; Dheeraj Peri; AmeyaShringi; Chi Zhang; Miguel Dominguez; Andreas Savakis; Raymond Ptucha
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3437
ER -
Shagan Sah, Dheeraj Peri, AmeyaShringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Raymond Ptucha. (2018). SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION. IEEE SigPort. http://sigport.org/3437
Shagan Sah, Dheeraj Peri, AmeyaShringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Raymond Ptucha, 2018. SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION. Available at: http://sigport.org/3437.
Shagan Sah, Dheeraj Peri, AmeyaShringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Raymond Ptucha. (2018). "SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION." Web.
1. Shagan Sah, Dheeraj Peri, AmeyaShringi, Chi Zhang, Miguel Dominguez, Andreas Savakis, Raymond Ptucha. SEMANTICALLY INVARIANT TEXT-TO-IMAGE GENERATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3437

Multimodal Reconstruction Using Vector Representation

Paper Details

Authors:
Shagan Sah, AmeyaShringi, DheerajPeri, John Hamilton, Andreas Savakis, Raymond Ptucha
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4 October 2018 - 11:24am
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ICIP_2018_Poster_2678.pdf

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[1] Shagan Sah, AmeyaShringi, DheerajPeri, John Hamilton, Andreas Savakis, Raymond Ptucha, "Multimodal Reconstruction Using Vector Representation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3435. Accessed: Nov. 13, 2018.
@article{3435-18,
url = {http://sigport.org/3435},
author = {Shagan Sah; AmeyaShringi; DheerajPeri; John Hamilton; Andreas Savakis; Raymond Ptucha },
publisher = {IEEE SigPort},
title = {Multimodal Reconstruction Using Vector Representation},
year = {2018} }
TY - EJOUR
T1 - Multimodal Reconstruction Using Vector Representation
AU - Shagan Sah; AmeyaShringi; DheerajPeri; John Hamilton; Andreas Savakis; Raymond Ptucha
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3435
ER -
Shagan Sah, AmeyaShringi, DheerajPeri, John Hamilton, Andreas Savakis, Raymond Ptucha. (2018). Multimodal Reconstruction Using Vector Representation. IEEE SigPort. http://sigport.org/3435
Shagan Sah, AmeyaShringi, DheerajPeri, John Hamilton, Andreas Savakis, Raymond Ptucha, 2018. Multimodal Reconstruction Using Vector Representation. Available at: http://sigport.org/3435.
Shagan Sah, AmeyaShringi, DheerajPeri, John Hamilton, Andreas Savakis, Raymond Ptucha. (2018). "Multimodal Reconstruction Using Vector Representation." Web.
1. Shagan Sah, AmeyaShringi, DheerajPeri, John Hamilton, Andreas Savakis, Raymond Ptucha. Multimodal Reconstruction Using Vector Representation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3435

AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES

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Authors:
Simone Bianco, Luigi Celona, Raimondo Schettini
Submitted On:
4 October 2018 - 11:21am
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poster1694

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[1] Simone Bianco, Luigi Celona, Raimondo Schettini, "AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3433. Accessed: Nov. 13, 2018.
@article{3433-18,
url = {http://sigport.org/3433},
author = {Simone Bianco; Luigi Celona; Raimondo Schettini },
publisher = {IEEE SigPort},
title = {AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES},
year = {2018} }
TY - EJOUR
T1 - AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES
AU - Simone Bianco; Luigi Celona; Raimondo Schettini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3433
ER -
Simone Bianco, Luigi Celona, Raimondo Schettini. (2018). AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES. IEEE SigPort. http://sigport.org/3433
Simone Bianco, Luigi Celona, Raimondo Schettini, 2018. AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES. Available at: http://sigport.org/3433.
Simone Bianco, Luigi Celona, Raimondo Schettini. (2018). "AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES." Web.
1. Simone Bianco, Luigi Celona, Raimondo Schettini. AESTHETICS ASSESSMENT OF IMAGES CONTAINING FACES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3433

Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity


We consider the problem of super-resolution for sub-diffraction imaging. We adapt conventional Fourier ptychographic approaches, for the case where the images to be acquired have an underlying structured sparsity. We propose some sub-sampling strategies which can be easily adapted to existing ptychographic setups. We then use a novel technique called CoPRAM with some modifications, to recover sparse (and block sparse) images from sub-sampled ptychographic measurements.

Paper Details

Authors:
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani
Submitted On:
30 April 2018 - 2:40pm
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slides-icassp18-nofigs.pdf

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[1] Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani, "Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3195. Accessed: Nov. 13, 2018.
@article{3195-18,
url = {http://sigport.org/3195},
author = {Gauri Jagatap; Zhengyu Chen; Chinmay Hegde; Namrata Vaswani },
publisher = {IEEE SigPort},
title = {Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity},
year = {2018} }
TY - EJOUR
T1 - Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity
AU - Gauri Jagatap; Zhengyu Chen; Chinmay Hegde; Namrata Vaswani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3195
ER -
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. (2018). Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity. IEEE SigPort. http://sigport.org/3195
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani, 2018. Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity. Available at: http://sigport.org/3195.
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. (2018). "Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity." Web.
1. Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3195

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: Nov. 13, 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

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