Inpainting and Denoising Challenges
Editors: Escalera, S., Ayache, S., Wan, J., Madadi, M., Güçlü, U., Baró, X. (Eds.)
Free Preview- 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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- About this book
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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)
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A Brief Review of Image Denoising Algorithms and Beyond
Pages 1-21
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ChaLearn Looking at People: Inpainting and Denoising Challenges
Pages 23-44
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U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting
Pages 45-50
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FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks
Pages 51-61
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Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising
Pages 63-76
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Table of contents (11 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Inpainting and Denoising Challenges
- Editors
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- 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
- Softcover ISBN
- 978-3-030-25616-6
- Series ISSN
- 2520-131X
- Edition Number
- 1
- Number of Pages
- VIII, 144
- Number of Illustrations
- 9 b/w illustrations, 56 illustrations in colour
- Topics
