Editors:
- Explores the latest trends in denoising and inpainting and goes beyond traditional methods in computer vision
- Presents solutions to fast (real time) and accurate automatic removal of occlusions (text, objects or stain) in images and video sequences
- Also surveys current state of the art on image and video inpainting, including further application domains, such as reconstruction of occluded and noisy data in medical imaging
Part of the book series: The Springer Series on Challenges in Machine Learning (SSCML)
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Table of contents (11 papers)
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Front Matter
About this book
The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting.
Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration.
This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapterspresent results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
Editors and Affiliations
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Department of Mathematics & Informatics, Universitat de Barcelona, Computer Vision Center, Barcelona, Spain
Sergio Escalera
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Aix-Marseille University, Marseille, France
Stephane Ayache
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Institute of Automation, Chinese Academy of Sciences, Beijing, China
Jun Wan
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Computer Vision Center, Autonomous University of Barcelona, Bellaterra, Spain
Meysam Madadi
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Radboud University Nijmegen, Nijmegen, The Netherlands
Umut Güçlü
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Open University of Catalonia, Barcelona, Spain
Xavier Baró
Bibliographic Information
Book Title: Inpainting and Denoising Challenges
Editors: Sergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, Xavier Baró
Series Title: The Springer Series on Challenges in Machine Learning
DOI: https://doi.org/10.1007/978-3-030-25614-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-25613-5Published: 17 October 2019
Softcover ISBN: 978-3-030-25616-6Published: 17 October 2020
eBook ISBN: 978-3-030-25614-2Published: 16 October 2019
Series ISSN: 2520-131X
Series E-ISSN: 2520-1328
Edition Number: 1
Number of Pages: VIII, 144
Number of Illustrations: 9 b/w illustrations, 56 illustrations in colour
Topics: Artificial Intelligence, Image Processing and Computer Vision, Pattern Recognition
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