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Inpainting and denoising challenges

Escalera, Sergio.

Inpainting and denoising challenges

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Inpainting and denoising challengesedited by Sergio Escalera ... [et al.].
其他作者:	Escalera, Sergio.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	viii, 144 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Signal processingDigital techniques.
電子資源:	https://doi.org/10.1007/978-3-030-25614-2
ISBN:	9783030256142$q(electronic bk.)

Inpainting and denoising challenges
Inpainting and denoising challenges[electronic resource] /edited by Sergio Escalera ... [et al.]. - Cham :Springer International Publishing :2019. - viii, 144 p. :ill. (some col.), digital ;24 cm. - The Springer series on challenges in machine learning,2520-131X. - Springer series on challenges in machine learning..

1. A Brief Review of Image Denoising Algorithms and Beyond -- 2. ChaLearn Looking at People: Inpainting and Denoising Challenges -- 3. U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting -- 4. FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks -- 5. Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising -- 6. Video DeCaptioning using U-Net with Stacked Dilated Convolutional Layers -- 7. Joint Caption Detection and Inpainting using Generative Network -- 8. Generative Image Inpainting for Person Pose Generation -- 9. Person Inpainting with Generative Adversarial Networks -- 10. Road Layout Understanding by Generative Adversarial Inpainting -- 11. Photo-realistic and Robust Inpainting of Faces using Refinement GANs.

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.

ISBN: 9783030256142$q(electronic bk.)

Standard No.: 10.1007/978-3-030-25614-2doiSubjects--Topical Terms:

182126
Signal processing
--Digital techniques.

LC Class. No.: TK5102.9 / .I56 2019

Dewey Class. No.: 621.3822

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520 $a 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.
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