Denoising of Photographic Images and Video [electronic resource] : Fundamentals, Open Challenges and New Trends /
Bertalmío, Marcelo.editor.(orcid)0000-0002-1023-8325https://orcid.org/0000-0002-1023-8325edt
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.
Includes bibliographical references and index.
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.
Optical data processing.
Image Processing and Computer Vision.