國立虎尾科技大學 |

Denoising of Photographic Images and Video = Fundamentals, Open Challenges and New Trends /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Denoising of Photographic Images and Video/ edited by Marcelo Bertalmío.
Reminder of title:	Fundamentals, Open Challenges and New Trends /
other author:	Bertalmío, Marcelo.
Description:	XIV, 333 p. 152 illus., 124 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Optical data processing. -
Online resource:	https://doi.org/10.1007/978-3-319-96029-6
ISBN:	9783319960296

Denoising of Photographic Images and Video = Fundamentals, Open Challenges and New Trends /
Denoising of Photographic Images and VideoFundamentals, Open Challenges and New Trends /[electronic resource] :edited by Marcelo Bertalmío. - 1st ed. 2018. - XIV, 333 p. 152 illus., 124 illus. in color.online resource. - Advances in Computer Vision and Pattern Recognition,2191-6586. - Advances in Computer Vision and Pattern Recognition,.

Modelling and Estimation of Signal-Dependent and Correlated Noise -- Sparsity-Based Denoising of Photographic Images: From Model-Based to Data-Driven -- Image Denoising – Old and New -- Convolutional Neural Networks for Image Denoising and Restoration -- Gaussian Priors for Image Denoising -- Internal Versus External Denoising – Benefits and Bounds -- Patch-Based Methods for Video Denoising -- Image and Video Noise: An Industry Perspective -- Noise Characteristics and Noise Perception -- Pull-Push Non-Local Means with Guided and Burst Filtering Capabilities -- Three Approaches to Improve Denoising Results that Do Not Involve Developing New Denoising Methods.

This unique text/reference presents a detailed review of noise removal for photographs and video. An international selection of expert contributors provide their insights into the fundamental challenges that remain in the field of denoising, examining how to properly model noise in real scenarios, how to tailor denoising algorithms to these models, and how to evaluate the results in a way that is consistent with perceived image quality. The book offers comprehensive coverage from problem formulation to the evaluation of denoising methods, from historical perspectives to state-of-the-art algorithms, and from fast real-time techniques that can be implemented in-camera to powerful and computationally intensive methods for off-line processing. Topics and features: Describes the basic methods for the analysis of signal-dependent and correlated noise, and the key concepts underlying sparsity-based image denoising algorithms Reviews the most successful variational approaches for image reconstruction, and introduces convolutional neural network-based denoising methods Provides an overview of the use of Gaussian priors for patch-based image denoising, and examines the potential of internal denoising Discusses selection and estimation strategies for patch-based video denoising, and explores how noise enters the imaging pipeline Surveys the properties of real camera noise, and outlines a fast approximation of nonlocal means filtering Proposes routes to improving denoising results via indirectly denoising a transform of the image, considering the right noise model and taking into account the perceived quality of the outputs This concise and clearly written volume will be of great value to researchers and professionals working in image processing and computer vision. The book will also serve as an accessible reference for advanced undergraduate and graduate students in computer science, applied mathematics, and related fields. Marcelo Bertalmío is a Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra, Barcelona, Spain.

ISBN: 9783319960296

Standard No.: 10.1007/978-3-319-96029-6doiSubjects--Topical Terms:

639187
Optical data processing.

LC Class. No.: TA1630-1650

Dewey Class. No.: 006.6

Denoising of Photographic Images and Video = Fundamentals, Open Challenges and New Trends /
LDR:04290nam a22004335i 4500 001 989825
003 DE-He213
005 20200702021350.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319960296 $9 978-3-319-96029-6
024 7 $a 10.1007/978-3-319-96029-6 $2 doi
035 $a 978-3-319-96029-6
050 4 $a TA1630-1650
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.6 $2 23
082 0 4 $a 006.37 $2 23
245 1 0 $a Denoising of Photographic Images and Video $h [electronic resource] : $b Fundamentals, Open Challenges and New Trends / $c edited by Marcelo Bertalmío.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIV, 333 p. 152 illus., 124 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Advances in Computer Vision and Pattern Recognition, $x 2191-6586
505 0 $a Modelling and Estimation of Signal-Dependent and Correlated Noise -- Sparsity-Based Denoising of Photographic Images: From Model-Based to Data-Driven -- Image Denoising – Old and New -- Convolutional Neural Networks for Image Denoising and Restoration -- Gaussian Priors for Image Denoising -- Internal Versus External Denoising – Benefits and Bounds -- Patch-Based Methods for Video Denoising -- Image and Video Noise: An Industry Perspective -- Noise Characteristics and Noise Perception -- Pull-Push Non-Local Means with Guided and Burst Filtering Capabilities -- Three Approaches to Improve Denoising Results that Do Not Involve Developing New Denoising Methods.
520 $a This unique text/reference presents a detailed review of noise removal for photographs and video. An international selection of expert contributors provide their insights into the fundamental challenges that remain in the field of denoising, examining how to properly model noise in real scenarios, how to tailor denoising algorithms to these models, and how to evaluate the results in a way that is consistent with perceived image quality. The book offers comprehensive coverage from problem formulation to the evaluation of denoising methods, from historical perspectives to state-of-the-art algorithms, and from fast real-time techniques that can be implemented in-camera to powerful and computationally intensive methods for off-line processing. Topics and features: Describes the basic methods for the analysis of signal-dependent and correlated noise, and the key concepts underlying sparsity-based image denoising algorithms Reviews the most successful variational approaches for image reconstruction, and introduces convolutional neural network-based denoising methods Provides an overview of the use of Gaussian priors for patch-based image denoising, and examines the potential of internal denoising Discusses selection and estimation strategies for patch-based video denoising, and explores how noise enters the imaging pipeline Surveys the properties of real camera noise, and outlines a fast approximation of nonlocal means filtering Proposes routes to improving denoising results via indirectly denoising a transform of the image, considering the right noise model and taking into account the perceived quality of the outputs This concise and clearly written volume will be of great value to researchers and professionals working in image processing and computer vision. The book will also serve as an accessible reference for advanced undergraduate and graduate students in computer science, applied mathematics, and related fields. Marcelo Bertalmío is a Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra, Barcelona, Spain.
650 0 $a Optical data processing. $3 639187
650 1 4 $a Image Processing and Computer Vision. $3 670819
700 1 $a Bertalmío, Marcelo. $e editor. $1 https://orcid.org/0000-0002-1023-8325 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1281647
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319960289
776 0 8 $i Printed edition: $z 9783319960302
776 0 8 $i Printed edition: $z 9783030071356
830 0 $a Advances in Computer Vision and Pattern Recognition, $x 2191-6586 $3 1256102
856 4 0 $u https://doi.org/10.1007/978-3-319-96029-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)

	簡單查詢
	進階查詢
	權威查詢
	圖書館SDI清單
	讀者推薦閱讀清單(公開)

	指定參考書
	圖書借閱排行榜
	圖書預約排行榜
	主題查詢
	分類瀏覽
	新書通報

	個人資料
	書籤
	薦購
	預約/借閱記錄查詢
	訊息