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Image/Video Processing

CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL


Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to address automatic occlusion detection and removal as a context-aware generic problem. In this paper, we present a novel methodology to identify objects that do not relate to the image context as occlusions and remove them, reconstructing the space occupied coherently.

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Authors:
Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo
Submitted On:
21 September 2019 - 12:17am
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CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL

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[1] Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo, "CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4800. Accessed: Sep. 21, 2019.
@article{4800-19,
url = {http://sigport.org/4800},
author = {Kumara Kahatapitiya; Dumindu Tissera; Ranga Rodrigo },
publisher = {IEEE SigPort},
title = {CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL},
year = {2019} }
TY - EJOUR
T1 - CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL
AU - Kumara Kahatapitiya; Dumindu Tissera; Ranga Rodrigo
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4800
ER -
Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo. (2019). CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL. IEEE SigPort. http://sigport.org/4800
Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo, 2019. CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL. Available at: http://sigport.org/4800.
Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo. (2019). "CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL." Web.
1. Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo. CONTEXT-AWARE AUTOMATIC OCCLUSION REMOVAL [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4800

CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY


Cloud segmentation is a vital task in applications that utilize satellite imagery. A common obstacle in using deep learning-based methods for this task is the insufficient number of images with their annotated ground truths. This work presents a content-aware unpaired image-to-image translation algorithm. It generates synthetic images with different land cover types from original images while preserving the locations and the intensity values of the cloud pixels. Therefore, no manual annotation of ground truth in these images is required.

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Authors:
Sorour Mohajerani, Reza Asad, Kumar Abhishek, Neha Sharma, Alysha van Duynhoven, Parvaneh Saeedi
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20 September 2019 - 8:05pm
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[1] Sorour Mohajerani, Reza Asad, Kumar Abhishek, Neha Sharma, Alysha van Duynhoven, Parvaneh Saeedi, "CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4798. Accessed: Sep. 21, 2019.
@article{4798-19,
url = {http://sigport.org/4798},
author = {Sorour Mohajerani; Reza Asad; Kumar Abhishek; Neha Sharma; Alysha van Duynhoven; Parvaneh Saeedi },
publisher = {IEEE SigPort},
title = {CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY},
year = {2019} }
TY - EJOUR
T1 - CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY
AU - Sorour Mohajerani; Reza Asad; Kumar Abhishek; Neha Sharma; Alysha van Duynhoven; Parvaneh Saeedi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4798
ER -
Sorour Mohajerani, Reza Asad, Kumar Abhishek, Neha Sharma, Alysha van Duynhoven, Parvaneh Saeedi. (2019). CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY. IEEE SigPort. http://sigport.org/4798
Sorour Mohajerani, Reza Asad, Kumar Abhishek, Neha Sharma, Alysha van Duynhoven, Parvaneh Saeedi, 2019. CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY. Available at: http://sigport.org/4798.
Sorour Mohajerani, Reza Asad, Kumar Abhishek, Neha Sharma, Alysha van Duynhoven, Parvaneh Saeedi. (2019). "CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY." Web.
1. Sorour Mohajerani, Reza Asad, Kumar Abhishek, Neha Sharma, Alysha van Duynhoven, Parvaneh Saeedi. CLOUDMASKGAN: A CONTENT-AWARE UNPAIRED IMAGE-TO-IMAGE TRANSLATION ALGORITHM FOR REMOTE SENSING IMAGERY [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4798

ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE

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Authors:
Lizhu Ye, Lei Zhu, Xuejing Kang, Anlong Ming
Submitted On:
20 September 2019 - 11:45am
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[1] Lizhu Ye, Lei Zhu, Xuejing Kang, Anlong Ming, "ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4789. Accessed: Sep. 21, 2019.
@article{4789-19,
url = {http://sigport.org/4789},
author = {Lizhu Ye; Lei Zhu; Xuejing Kang; Anlong Ming },
publisher = {IEEE SigPort},
title = {ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE},
year = {2019} }
TY - EJOUR
T1 - ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE
AU - Lizhu Ye; Lei Zhu; Xuejing Kang; Anlong Ming
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4789
ER -
Lizhu Ye, Lei Zhu, Xuejing Kang, Anlong Ming. (2019). ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE. IEEE SigPort. http://sigport.org/4789
Lizhu Ye, Lei Zhu, Xuejing Kang, Anlong Ming, 2019. ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE. Available at: http://sigport.org/4789.
Lizhu Ye, Lei Zhu, Xuejing Kang, Anlong Ming. (2019). "ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE." Web.
1. Lizhu Ye, Lei Zhu, Xuejing Kang, Anlong Ming. ADAPTIVE OCCLUSION BOUNDARY EXTRACTION FOR DEPTH INFERENCE [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4789

RGB-D tracking of complex shapes using coarse object models


This paper presents a framework for accurately tracking objects of complex shapes with joint minimization of geometric and photometric parameters using a coarse 3D object model with the RGB-D cameras. Tracking with coarse 3D model is remarkably useful for industrial applications. A technique is proposed that uses a combination of point-to-plane distance minimization and photometric error minimization to track objects accurately. The concept of 'keyframes' are used in this system of object tracking for minimizing drift.

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Authors:
Agniva Sengupta, Alexandre Krupa, Eric Marchand
Submitted On:
20 September 2019 - 11:29am
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[1] Agniva Sengupta, Alexandre Krupa, Eric Marchand, "RGB-D tracking of complex shapes using coarse object models", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4786. Accessed: Sep. 21, 2019.
@article{4786-19,
url = {http://sigport.org/4786},
author = {Agniva Sengupta; Alexandre Krupa; Eric Marchand },
publisher = {IEEE SigPort},
title = {RGB-D tracking of complex shapes using coarse object models},
year = {2019} }
TY - EJOUR
T1 - RGB-D tracking of complex shapes using coarse object models
AU - Agniva Sengupta; Alexandre Krupa; Eric Marchand
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4786
ER -
Agniva Sengupta, Alexandre Krupa, Eric Marchand. (2019). RGB-D tracking of complex shapes using coarse object models. IEEE SigPort. http://sigport.org/4786
Agniva Sengupta, Alexandre Krupa, Eric Marchand, 2019. RGB-D tracking of complex shapes using coarse object models. Available at: http://sigport.org/4786.
Agniva Sengupta, Alexandre Krupa, Eric Marchand. (2019). "RGB-D tracking of complex shapes using coarse object models." Web.
1. Agniva Sengupta, Alexandre Krupa, Eric Marchand. RGB-D tracking of complex shapes using coarse object models [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4786

A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix


A new hyperspectral texture descriptor, Relocated Spectral Difference Occurrence Matrix (rSDOM) is proposed. It assesses the distribution of spectral difference in a given neighborhood. For metrological purposes, rSDOM employs Kullback-Leibler pseudo-divergence (KLPD) for spectral difference calculation. It is generic and adapted for any spectral range and number of band. As validation, a texture classification scheme based on nearest neighbor classifier is applied on HyTexiLa dataset using rSDOM.

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Authors:
Rui Jian Chu, Noel Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg
Submitted On:
20 September 2019 - 11:28am
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[1] Rui Jian Chu, Noel Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg, "A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4785. Accessed: Sep. 21, 2019.
@article{4785-19,
url = {http://sigport.org/4785},
author = {Rui Jian Chu; Noel Richard; Christine Fernandez-Maloigne; Jon Yngve Hardeberg },
publisher = {IEEE SigPort},
title = {A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix},
year = {2019} }
TY - EJOUR
T1 - A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix
AU - Rui Jian Chu; Noel Richard; Christine Fernandez-Maloigne; Jon Yngve Hardeberg
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4785
ER -
Rui Jian Chu, Noel Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg. (2019). A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix. IEEE SigPort. http://sigport.org/4785
Rui Jian Chu, Noel Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg, 2019. A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix. Available at: http://sigport.org/4785.
Rui Jian Chu, Noel Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg. (2019). "A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix." Web.
1. Rui Jian Chu, Noel Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg. A Metrological Measurement of Texture in Hyperspectral Images using Relocated Spectral Difference Occurrence Matrix [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4785

Long & Short Memory Balancing In Visual Co-tracking using Q-learning


Employing one or more additional classifiers to break the self-learning loop in tracing-by-detection has gained considerable attention. Most of such trackers merely utilize the redundancy to address the accumulating label error in the tracking loop, and suffer from high computational complexity as well as tracking challenges that may interrupt all classifiers (e.g. temporal occlusions). We propose the active co-tracking framework, in which the main classifier of the tracker labels samples of the video sequence, and only consults auxiliary classifier when it is uncertain.

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Authors:
Maryam Sadat Mirzaei, Shigeyuki Oba
Submitted On:
20 September 2019 - 9:57am
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[1] Maryam Sadat Mirzaei, Shigeyuki Oba, "Long & Short Memory Balancing In Visual Co-tracking using Q-learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4780. Accessed: Sep. 21, 2019.
@article{4780-19,
url = {http://sigport.org/4780},
author = {Maryam Sadat Mirzaei; Shigeyuki Oba },
publisher = {IEEE SigPort},
title = {Long & Short Memory Balancing In Visual Co-tracking using Q-learning},
year = {2019} }
TY - EJOUR
T1 - Long & Short Memory Balancing In Visual Co-tracking using Q-learning
AU - Maryam Sadat Mirzaei; Shigeyuki Oba
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4780
ER -
Maryam Sadat Mirzaei, Shigeyuki Oba. (2019). Long & Short Memory Balancing In Visual Co-tracking using Q-learning. IEEE SigPort. http://sigport.org/4780
Maryam Sadat Mirzaei, Shigeyuki Oba, 2019. Long & Short Memory Balancing In Visual Co-tracking using Q-learning. Available at: http://sigport.org/4780.
Maryam Sadat Mirzaei, Shigeyuki Oba. (2019). "Long & Short Memory Balancing In Visual Co-tracking using Q-learning." Web.
1. Maryam Sadat Mirzaei, Shigeyuki Oba. Long & Short Memory Balancing In Visual Co-tracking using Q-learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4780

A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS


Natural scene statistics (NSS) model has received considerable attention in the image quality assessment (IQA) community due to its high sensitivity to image distortion. However, most existing NSS-based IQA methods extract features either from spatial domain or from transform domain. There is little work to simultaneously consider the features from these two domains. In this paper, a novel blind IQA method (NBIQA) based on refined NSS is proposed. The proposed NBIQA first investigates the performance of a large number of candidate features from both the spatial and transform domains.

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Authors:
Fu-Zhao Ou, Yuan-Gen Wang, Guopu Zhu
Submitted On:
20 September 2019 - 9:56am
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[1] Fu-Zhao Ou, Yuan-Gen Wang, Guopu Zhu, "A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4779. Accessed: Sep. 21, 2019.
@article{4779-19,
url = {http://sigport.org/4779},
author = {Fu-Zhao Ou; Yuan-Gen Wang; Guopu Zhu },
publisher = {IEEE SigPort},
title = {A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS},
year = {2019} }
TY - EJOUR
T1 - A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS
AU - Fu-Zhao Ou; Yuan-Gen Wang; Guopu Zhu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4779
ER -
Fu-Zhao Ou, Yuan-Gen Wang, Guopu Zhu. (2019). A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS. IEEE SigPort. http://sigport.org/4779
Fu-Zhao Ou, Yuan-Gen Wang, Guopu Zhu, 2019. A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS. Available at: http://sigport.org/4779.
Fu-Zhao Ou, Yuan-Gen Wang, Guopu Zhu. (2019). "A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS." Web.
1. Fu-Zhao Ou, Yuan-Gen Wang, Guopu Zhu. A NOVEL BLIND IMAGE QUALITY ASSESSMENT METHOD BASED ON REFINED NATURAL SCENE STATISTICS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4779

Adaptive Hard Example Mining for Image Captioning

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20 September 2019 - 8:46am
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[1] , "Adaptive Hard Example Mining for Image Captioning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4778. Accessed: Sep. 21, 2019.
@article{4778-19,
url = {http://sigport.org/4778},
author = { },
publisher = {IEEE SigPort},
title = {Adaptive Hard Example Mining for Image Captioning},
year = {2019} }
TY - EJOUR
T1 - Adaptive Hard Example Mining for Image Captioning
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4778
ER -
. (2019). Adaptive Hard Example Mining for Image Captioning. IEEE SigPort. http://sigport.org/4778
, 2019. Adaptive Hard Example Mining for Image Captioning. Available at: http://sigport.org/4778.
. (2019). "Adaptive Hard Example Mining for Image Captioning." Web.
1. . Adaptive Hard Example Mining for Image Captioning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4778

AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING


Recently, deep-learning based reconstruction models have been proposed to improve recovery performance of compressive sensed image and overcome expensive time complexity drawbacks of iteration-based traditional algorithms. In this paper, we propose an end-to-end multi-scale residual convolutional neural network (CNN), dubbed MSRNet, to simulate image compressive sensing (CS) and inverse reconstruction process in real situation. In MSRNet, we apply three parallel channels with different convolution kernel sizes to exploit different-scale feature information.

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Authors:
Sumei Li, Chunping Hou
Submitted On:
20 September 2019 - 8:10am
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[1] Sumei Li, Chunping Hou, "AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4777. Accessed: Sep. 21, 2019.
@article{4777-19,
url = {http://sigport.org/4777},
author = {Sumei Li; Chunping Hou },
publisher = {IEEE SigPort},
title = {AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING},
year = {2019} }
TY - EJOUR
T1 - AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING
AU - Sumei Li; Chunping Hou
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4777
ER -
Sumei Li, Chunping Hou. (2019). AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING. IEEE SigPort. http://sigport.org/4777
Sumei Li, Chunping Hou, 2019. AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING. Available at: http://sigport.org/4777.
Sumei Li, Chunping Hou. (2019). "AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING." Web.
1. Sumei Li, Chunping Hou. AN END-TO-END MULTI-SCALE RESIDUAL RECONSTRUCTION NETWORK FOR IMAGE COMPRESSIVE SENSING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4777

PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION


Existing SISR (single image super-resolution) methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from its high-resolution (HR) counterpart, which inevitably give rise to poor performance when the degradation is out of assumption. To address this issue, we propose a framework PRED (parallel residual and encoder-decoder network) with an innovative training strategy to enhance the robustness to multiple degradations. Consequently, the network can handle spatially variant degradations, which significantly improves the practicability of the proposed method.

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Authors:
Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen
Submitted On:
20 September 2019 - 7:16am
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[1] Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen, "PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4775. Accessed: Sep. 21, 2019.
@article{4775-19,
url = {http://sigport.org/4775},
author = {Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen },
publisher = {IEEE SigPort},
title = {PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION},
year = {2019} }
TY - EJOUR
T1 - PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION
AU - Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4775
ER -
Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen. (2019). PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION. IEEE SigPort. http://sigport.org/4775
Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen, 2019. PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION. Available at: http://sigport.org/4775.
Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen. (2019). "PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION." Web.
1. Guangyang Wu; Lili Zhao; Wenyi Wang; Liaoyuan Zeng; Jianwen Chen. PRED: A PARALLEL NETWORK FOR HANDLING MULTIPLE DEGRADATIONS VIA SINGLE MODEL IN SINGLE IMAGE SUPER-RESOLUTION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4775

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