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Inpainting and Denoising Challenges

Name: Inpainting and Denoising Challenges
Brand: Springer Nature Switzerland AG
SKU: 33305611
Price: 51.28 EUR
Availability: InStock
Author: Stephane Ayache
ISBN: 9783030256166

Language

English

Book Paperback

Libristo code: 33305611

Publishers Springer Nature Switzerland AG, October 2020

The problem of dealing with missing or incomplete data in machine learning and computer vision arise... Full description

Libristo code: 33305611

125 b

51.28 €

100.30 лв

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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.