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, appliedmathematics, and related fields.

Marcelo Bertalmío is a Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra, Barcelona, Spain.