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Author
Patricia L. Suarez
;
Angel D. Sappa
;
Boris X. Vintimilla
Title
Cross-spectral image dehaze through a dense stacked conditional GAN based approach.
Type
Conference Article
Year
2018
Publication
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018)
Abbreviated Journal
Volume
Issue
Pages
358-364
Keywords
Abstract
This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
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Conference
Notes
Approved
no
Call Number
gtsi @ user @
Serial
92
Permanent link to this record
Author
Jorge L. Charco
;
Boris X. Vintimilla
;
Angel D. Sappa
Title
Deep learning based camera pose estimation in multi-view environment.
Type
Conference Article
Year
2018
Publication
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018)
Abbreviated Journal
Volume
Issue
Pages
224-228
Keywords
Abstract
This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
Address
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Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
Approved
no
Call Number
gtsi @ user @
Serial
93
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Author
Xavier Soria
;
Angel D. Sappa
;
Riad Hammoud
Title
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059.
Type
Journal Article
Year
2018
Publication
Abbreviated Journal
Volume
Vol. 18
Issue
Issue 7
Pages
Keywords
Abstract
Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
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Notes
Approved
no
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gtsi @ user @
Serial
96
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