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Inpainting and Denoising Challenges

  • Sergio Escalera
  • Stephane Ayache
  • Jun Wan
  • Meysam Madadi
  • Umut Güçlü
  • Xavier Baró
Conference proceedings

Table of contents

  1. Front Matter
    Pages i-viii
  2. Shuhang Gu, Radu Timofte
    Pages 1-21
  3. Sergio Escalera, Martí Soler, Stephane Ayache, Umut Güçlü, Jun Wan, Meysam Madadi et al.
    Pages 23-44
  4. Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang, Ding Liu, Anxiao (Andrew) Jiang
    Pages 45-50
  5. Shivansh Mundra, Arnav Kumar Jain, Sayan Sinha
    Pages 77-86
  6. Vismay Patel, Anubha Pandey
    Pages 87-94
  7. Vismay Patel, Anubha Pandey
    Pages 95-100
  8. Lorenzo Berlincioni, Federico Becattini, Leonardo Galteri, Lorenzo Seidenari, Alberto Del Bimbo
    Pages 111-128
  9. Dejan Malesevic, Christoph Mayer, Shuhang Gu, Radu Timofte
    Pages 129-144

About these proceedings

Introduction

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. 

Keywords

Machine Learning Computer vision Image processing Video processing Video de-capturing Noisy data Occluded data Medical imaging Inpainting denoising

Editors and affiliations

  • Sergio Escalera
    • 1
  • Stephane Ayache
    • 2
  • Jun Wan
    • 3
  • Meysam Madadi
    • 4
  • Umut Güçlü
    • 5
  • Xavier Baró
    • 6
  1. 1.Department of Mathematics & InformaticsUniversitat de Barcelona, Computer Vision CenterBarcelonaSpain
  2. 2.Aix-Marseille UniversityMarseilleFrance
  3. 3.Institute of AutomationChinese Academy of SciencesBeijingChina
  4. 4.Computer Vision CenterAutonomous University of BarcelonaBellaterraSpain
  5. 5.Radboud University NijmegenNijmegenThe Netherlands
  6. 6.Open University of CataloniaBarcelonaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-25614-2
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-25613-5
  • Online ISBN 978-3-030-25614-2
  • Series Print ISSN 2520-131X
  • Series Online ISSN 2520-1328
  • Buy this book on publisher's site