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ICIP 2019

The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website.

Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction


Inpainting-based compression has been suggested as a qualitative competitor to the JPEG family of transform-based codecs, specifically for high compression ratios. However, it also requires sophisticated interpolation, data optimisation and encoding tasks that are both slow and hard to implement. We propose a fast and simple alternative that combines Shepard interpolation with a novel joint inpainting and prediction approach. It represents the image by a fraction of its pixel values on a sparse regular subgrid that are selected by an efficient optimisation strategy.

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Authors:
Pascal Peter
Submitted On:
12 September 2019 - 9:03am
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[1] Pascal Peter, "Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4604. Accessed: Sep. 15, 2019.
@article{4604-19,
url = {http://sigport.org/4604},
author = {Pascal Peter },
publisher = {IEEE SigPort},
title = {Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction},
year = {2019} }
TY - EJOUR
T1 - Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction
AU - Pascal Peter
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4604
ER -
Pascal Peter. (2019). Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction. IEEE SigPort. http://sigport.org/4604
Pascal Peter, 2019. Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction. Available at: http://sigport.org/4604.
Pascal Peter. (2019). "Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction." Web.
1. Pascal Peter. Fast Inpainting-based Compression: Combinging Shepard Interpolation with Joint Inpainting and Prediction [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4604

ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET

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Authors:
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang
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12 September 2019 - 6:38am
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[1] Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang, "ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4603. Accessed: Sep. 15, 2019.
@article{4603-19,
url = {http://sigport.org/4603},
author = {Shaobo Min; Xuejin Chen; Hongtao Xie; Zheng-Jun Zha; Guoqiang Bi; Feng Wu; Yongdong Zhang },
publisher = {IEEE SigPort},
title = {ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET},
year = {2019} }
TY - EJOUR
T1 - ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET
AU - Shaobo Min; Xuejin Chen; Hongtao Xie; Zheng-Jun Zha; Guoqiang Bi; Feng Wu; Yongdong Zhang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4603
ER -
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang. (2019). ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET. IEEE SigPort. http://sigport.org/4603
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang, 2019. ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET. Available at: http://sigport.org/4603.
Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang. (2019). "ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET." Web.
1. Shaobo Min, Xuejin Chen, Hongtao Xie, Zheng-Jun Zha, Guoqiang Bi, Feng Wu, Yongdong Zhang. ACCURATE SEGMENTATION OF SYNAPTIC CLEFT WITH CONTOUR GROWING CONCATENATED WITH A CONVNET [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4603

Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy

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12 September 2019 - 5:24am
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[1] , "Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4602. Accessed: Sep. 15, 2019.
@article{4602-19,
url = {http://sigport.org/4602},
author = { },
publisher = {IEEE SigPort},
title = {Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy},
year = {2019} }
TY - EJOUR
T1 - Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4602
ER -
. (2019). Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy. IEEE SigPort. http://sigport.org/4602
, 2019. Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy. Available at: http://sigport.org/4602.
. (2019). "Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy." Web.
1. . Dense Optical Flow for the Reconstruction of Weakly Textured and Structured Surfaces: Application to Endoscopy [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4602

INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS


The goal of endoscopic analysis is to find abnormal lesions and determine further therapy from the obtained information. However, the procedure produces a variety of non-informative frames and lesions can be missed due to poor video quality. Especially when analyzing entire endoscopic videos made by non-expert endoscopists, informative frame classification is crucial to e.g. video quality grading. This work concentrates on the design of an automated indication of informativeness of video frames.

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Authors:
Jeroen de groof, Fons van der Sommen, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, Peter de with
Submitted On:
12 September 2019 - 2:18am
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[1] Jeroen de groof, Fons van der Sommen, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, Peter de with, "INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4601. Accessed: Sep. 15, 2019.
@article{4601-19,
url = {http://sigport.org/4601},
author = {Jeroen de groof; Fons van der Sommen; Maarten Struyvenberg; Svitlana Zinger; Wouter Curvers; Erik Schoon; Jacques Bergman; Peter de with },
publisher = {IEEE SigPort},
title = {INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS},
year = {2019} }
TY - EJOUR
T1 - INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS
AU - Jeroen de groof; Fons van der Sommen; Maarten Struyvenberg; Svitlana Zinger; Wouter Curvers; Erik Schoon; Jacques Bergman; Peter de with
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4601
ER -
Jeroen de groof, Fons van der Sommen, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, Peter de with. (2019). INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS. IEEE SigPort. http://sigport.org/4601
Jeroen de groof, Fons van der Sommen, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, Peter de with, 2019. INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS. Available at: http://sigport.org/4601.
Jeroen de groof, Fons van der Sommen, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, Peter de with. (2019). "INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS." Web.
1. Jeroen de groof, Fons van der Sommen, Maarten Struyvenberg, Svitlana Zinger, Wouter Curvers, Erik Schoon, Jacques Bergman, Peter de with. INFORMATIVE FRAME CLASSIFICATION OF ENDOSCOPIC VIDEOS USING CONVOLUTIONAL NEURAL NETWORKS AND HIDDEN MARKOV MODELS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4601

Two-stage Unsupervised Learning Method for Affine and Deformable Registration


Conventional medical image registration relies on time-consuming iterative optimization. We propose a two-stage unsupervised learning method for 3D medical image registration. In the first stage, we learn a global image-wise affine map by a deep network. In the second stage, we learn a local voxel-wise deformation vector field by an encoder-decoder architecture. The final registered image is acquired by applying the local deformation field to the moved image of the first stage.

Paper Details

Authors:
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan
Submitted On:
12 September 2019 - 1:32am
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[1] Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan, "Two-stage Unsupervised Learning Method for Affine and Deformable Registration", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4600. Accessed: Sep. 15, 2019.
@article{4600-19,
url = {http://sigport.org/4600},
author = {Dongdong Gu; Guocai Liu; Juanxiu Tian; Qi Zhan },
publisher = {IEEE SigPort},
title = {Two-stage Unsupervised Learning Method for Affine and Deformable Registration},
year = {2019} }
TY - EJOUR
T1 - Two-stage Unsupervised Learning Method for Affine and Deformable Registration
AU - Dongdong Gu; Guocai Liu; Juanxiu Tian; Qi Zhan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4600
ER -
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan. (2019). Two-stage Unsupervised Learning Method for Affine and Deformable Registration. IEEE SigPort. http://sigport.org/4600
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan, 2019. Two-stage Unsupervised Learning Method for Affine and Deformable Registration. Available at: http://sigport.org/4600.
Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan. (2019). "Two-stage Unsupervised Learning Method for Affine and Deformable Registration." Web.
1. Dongdong Gu, Guocai Liu, Juanxiu Tian, Qi Zhan. Two-stage Unsupervised Learning Method for Affine and Deformable Registration [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4600

ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS


Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field. However, CNN incurs high computational complexity, especially for vision quality applications because of large image resolution. In this paper, we propose an iterative architecture-aware pruning algorithm with adaptive magnitude threshold while cooperating with quality-metric measurement simultaneously. We show the performance improvement applied on vision quality applications and provide comprehensive analysis with flexible pruning configuration.

Paper Details

Authors:
Wei-Ting Wang, Han-Lin Li, Wei-Shiang Lin, Cheng-Ming Chiang, Yi-Min Tsai
Submitted On:
12 September 2019 - 1:21am
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[1] Wei-Ting Wang, Han-Lin Li, Wei-Shiang Lin, Cheng-Ming Chiang, Yi-Min Tsai, "ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4599. Accessed: Sep. 15, 2019.
@article{4599-19,
url = {http://sigport.org/4599},
author = {Wei-Ting Wang; Han-Lin Li; Wei-Shiang Lin; Cheng-Ming Chiang; Yi-Min Tsai },
publisher = {IEEE SigPort},
title = {ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS},
year = {2019} }
TY - EJOUR
T1 - ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS
AU - Wei-Ting Wang; Han-Lin Li; Wei-Shiang Lin; Cheng-Ming Chiang; Yi-Min Tsai
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4599
ER -
Wei-Ting Wang, Han-Lin Li, Wei-Shiang Lin, Cheng-Ming Chiang, Yi-Min Tsai. (2019). ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS. IEEE SigPort. http://sigport.org/4599
Wei-Ting Wang, Han-Lin Li, Wei-Shiang Lin, Cheng-Ming Chiang, Yi-Min Tsai, 2019. ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS. Available at: http://sigport.org/4599.
Wei-Ting Wang, Han-Lin Li, Wei-Shiang Lin, Cheng-Ming Chiang, Yi-Min Tsai. (2019). "ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS." Web.
1. Wei-Ting Wang, Han-Lin Li, Wei-Shiang Lin, Cheng-Ming Chiang, Yi-Min Tsai. ARCHITECTURE-AWARE NETWORK PRUNING FOR VISION QUALITY APPLICATIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4599

MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION


The recently-proposed reinforcement learning for mapless visual navigation can generate an optimal policy for searching different targets. However, most state-of-the-art deep reinforcement learning (DRL) models depend on hard rewards to learn the optimal policy, which can lead to the lack of previous diverse experiences. Moreover, these pre-trained DRL models cannot generalize well to un-trained tasks. To overcome these problems above, in this paper, we propose a Memorybased Parameterized Skills Learning (MPSL) model for mapless visual navigation.

Paper Details

Authors:
Yuyang Liu, Yang Cong and Gan Sun
Submitted On:
11 September 2019 - 11:06pm
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[1] Yuyang Liu, Yang Cong and Gan Sun, "MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4598. Accessed: Sep. 15, 2019.
@article{4598-19,
url = {http://sigport.org/4598},
author = {Yuyang Liu; Yang Cong and Gan Sun },
publisher = {IEEE SigPort},
title = {MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION},
year = {2019} }
TY - EJOUR
T1 - MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION
AU - Yuyang Liu; Yang Cong and Gan Sun
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4598
ER -
Yuyang Liu, Yang Cong and Gan Sun. (2019). MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION. IEEE SigPort. http://sigport.org/4598
Yuyang Liu, Yang Cong and Gan Sun, 2019. MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION. Available at: http://sigport.org/4598.
Yuyang Liu, Yang Cong and Gan Sun. (2019). "MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION." Web.
1. Yuyang Liu, Yang Cong and Gan Sun. MEMORY-BASED PARAMETERIZED SKILLS LEARNING FOR MAPLESS VISUAL NAVIGATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4598

Evaluating Crowd Density Estimators via Their Uncertainty Bounds

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Authors:
Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hegarat
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11 September 2019 - 4:52pm
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[1] Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hegarat, "Evaluating Crowd Density Estimators via Their Uncertainty Bounds", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4597. Accessed: Sep. 15, 2019.
@article{4597-19,
url = {http://sigport.org/4597},
author = {Jennifer Vandoni; Emanuel Aldea; Sylvie Le Hegarat },
publisher = {IEEE SigPort},
title = {Evaluating Crowd Density Estimators via Their Uncertainty Bounds},
year = {2019} }
TY - EJOUR
T1 - Evaluating Crowd Density Estimators via Their Uncertainty Bounds
AU - Jennifer Vandoni; Emanuel Aldea; Sylvie Le Hegarat
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4597
ER -
Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hegarat. (2019). Evaluating Crowd Density Estimators via Their Uncertainty Bounds. IEEE SigPort. http://sigport.org/4597
Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hegarat, 2019. Evaluating Crowd Density Estimators via Their Uncertainty Bounds. Available at: http://sigport.org/4597.
Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hegarat. (2019). "Evaluating Crowd Density Estimators via Their Uncertainty Bounds." Web.
1. Jennifer Vandoni, Emanuel Aldea, Sylvie Le Hegarat. Evaluating Crowd Density Estimators via Their Uncertainty Bounds [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4597

A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES


For images with congested scenes, the task of crowd analysis,
including crowd counting and crowd distribution prediction,
becomes very difficult. To address these issues, various
CNN-based approaches have been proposed. However, those
methods usually have a large number of parameters and require
huge computing resources. In this paper, we focus on
low-complexity approaches and propose a lightweight endto-
end network for crowd analysis. Our method utilizes an
effective scale-aware module to extract multi-scale features

Paper Details

Authors:
Xiangyu Ma, Shan Du, Yu Liu
Submitted On:
11 September 2019 - 2:29pm
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[1] Xiangyu Ma, Shan Du, Yu Liu, "A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4596. Accessed: Sep. 15, 2019.
@article{4596-19,
url = {http://sigport.org/4596},
author = {Xiangyu Ma; Shan Du; Yu Liu },
publisher = {IEEE SigPort},
title = {A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES},
year = {2019} }
TY - EJOUR
T1 - A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES
AU - Xiangyu Ma; Shan Du; Yu Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4596
ER -
Xiangyu Ma, Shan Du, Yu Liu. (2019). A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES. IEEE SigPort. http://sigport.org/4596
Xiangyu Ma, Shan Du, Yu Liu, 2019. A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES. Available at: http://sigport.org/4596.
Xiangyu Ma, Shan Du, Yu Liu. (2019). "A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES." Web.
1. Xiangyu Ma, Shan Du, Yu Liu. A LIGHTWEIGHT NEURAL NETWORK FOR CROWD ANALYSIS OF IMAGES WITH CONGESTED SCENES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4596

TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION


A volumetric non-blind single image super-resolution technique using tensor factorization has been recently introduced by our group. That method allowed a 2-order-of-magnitude faster high-resolution image reconstruction with equivalent image quality compared to state-of-the-art algorithms. In this work a joint alternating recovery of the high-resolution image and of the unknown point spread function parameters is proposed. The method is evaluated on dental computed tomography images.

Paper Details

Authors:
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé
Submitted On:
11 September 2019 - 12:17pm
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[1] Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé, "TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4595. Accessed: Sep. 15, 2019.
@article{4595-19,
url = {http://sigport.org/4595},
author = {Janka Hatvani; Adrian Basarab; Jérome Michetti; Miklós Gyöngy; Denis Kouamé },
publisher = {IEEE SigPort},
title = {TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION},
year = {2019} }
TY - EJOUR
T1 - TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION
AU - Janka Hatvani; Adrian Basarab; Jérome Michetti; Miklós Gyöngy; Denis Kouamé
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4595
ER -
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé. (2019). TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION. IEEE SigPort. http://sigport.org/4595
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé, 2019. TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION. Available at: http://sigport.org/4595.
Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé. (2019). "TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION." Web.
1. Janka Hatvani, Adrian Basarab, Jérome Michetti, Miklós Gyöngy, Denis Kouamé. TENSOR-FACTORIZATION-BASED 3D SINGLE IMAGE SUPER-RESOLUTION WITH SEMI-BLIND POINT SPREAD FUNCTION ESTIMATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4595

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