The Springer Series on Challenges in Machine Learning

Inpainting and Denoising Challenges

Editors: Escalera, S., Ayache, S., Wan, J., Madadi, M., Güçlü, U., Baró, X. (Eds.)

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  • 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
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eBook 63,06 €
price for Spain (gross)
  • ISBN 978-3-030-25614-2
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 77,99 €
price for Spain (gross)
  • ISBN 978-3-030-25613-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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 chapters present 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. 

Table of contents (11 chapters)

Table of contents (11 chapters)
  • A Brief Review of Image Denoising Algorithms and Beyond

    Pages 1-21

    Gu, Shuhang (et al.)

  • ChaLearn Looking at People: Inpainting and Denoising Challenges

    Pages 23-44

    Escalera, Sergio (et al.)

  • U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

    Pages 45-50

    Prabhu, Ramakrishna (et al.)

  • FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks

    Pages 51-61

    Adiga V, Sukesh (et al.)

  • Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising

    Pages 63-76

    Quan, Le Manh (et al.)

Buy this book

eBook 63,06 €
price for Spain (gross)
  • ISBN 978-3-030-25614-2
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 77,99 €
price for Spain (gross)
  • ISBN 978-3-030-25613-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

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
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-25614-2
DOI
10.1007/978-3-030-25614-2
Hardcover ISBN
978-3-030-25613-5
Series ISSN
2520-131X
Edition Number
1
Number of Pages
VIII, 144
Number of Illustrations
9 b/w illustrations, 56 illustrations in colour
Topics