Signal Processing For Image Enhancement And Multimedia Processing

This is an edited volume, written by well-recognized international researchers with extended chapter style versions of the best papers presented at the SITIS 2006 International Conference. This book presents the state-of-the-art and recent research results on the application of advanced signal processing techniques for improving the value of image and video data. It introduces new results on video coding on time-honored topic of securing image information. The book is designed for a professional audience composed of practitioners and researchers in industry. This book is also suitable for advanced-level students in computer science.

This book presents collective works published in the recent Special Issue (SI) entitled " Digital Signal, Image and Video Processing for Emerging Multimedia Technology". These works address the emerging technology in signal processing and its new aspects, as well as the related applications. Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology. The applications vary and range from digital signal processing to image, video and multimedia signal processing, also including object classification, learning mechanism design and data security. Recent advances in numerical, theoretical and experimental methodologies are presented within the scope of the current book, along with the finding of new learning methods and new methodological developments and their limitations. This book brings together a collection of inter-/multidisciplinary works applied to many classification and data security applications in a coherent manner.

Real-Time Image and Video Processing presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods.Throughout the book, carefully selected, representative examples from the literature are presented to illustrate the discussed concepts. After reading the book, readers will have a strong understanding of the wide variety of techniques and tools involved in designing a real-time image or video processing system.

Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.

Multimedia stands as one of the most challenging and exciting aspects of the information era. Although there are books available that deal with various facets of multimedia, the field has urgently needed a comprehensive look at recent developments in the systems, processing, and applications of image and video data in a multimedia environment.

Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines. This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters. Part 2: Model-based Signal Processing covers probability and information models, Bayesian inference, Wiener filter, adaptive filters, linear prediction hidden Markov models and independent component analysis. Part 3: Applications of Signal Processing in Speech, Music and Telecommunications explains the topics of speech and music processing, echo cancellation, deconvolution and channel equalization, and mobile communication signal processing. Covers music signal processing, explains the anatomy and psychoacoustics of hearing and the design of MP3 music coder Examines speech processing technology including speech models, speech coding for mobile phones and speech recognition Covers single-input and multiple-inputs denoising methods, bandwidth extension and the recovery of lost speech packets in applications such as voice over IP (VoIP) Illustrated throughout, including numerous solved problems, Matlab experiments and demonstrations Companion website features Matlab and C++ programs with electronic copies of all figures. This book is ideal for researchers, postgraduates and senior undergraduates in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be a valuable text to professional engineers in telecommunications and audio and signal processing industries.

This book presents collective works published in the recent Special Issue (SI) entitled " Digital Signal, Image and Video Processing for Emerging Multimedia Technology". These works address the emerging technology in signal processing and its new aspects, as well as the related applications. Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology. The applications vary and range from digital signal processing to image, video and multimedia signal processing, also including object classification, learning mechanism design and data security. Recent advances in numerical, theoretical and experimental methodologies are presented within the scope of the current book, along with the finding of new learning methods and new methodological developments and their limitations. This book brings together a collection of inter-/multidisciplinary works applied to many classification and data security applications in a coherent manner.

Digital Image Enhancement and Reconstruction: Techniques and Applications explores different concepts and techniques used for the enhancement as well as reconstruction of low-quality images. Most real-life applications require good quality images to gain maximum performance, however, the quality of the images captured in real-world scenarios is often very unsatisfactory. Most commonly, images are noisy, blurry, hazy, tiny, and hence need to pass through image enhancement and/or reconstruction algorithms before they can be processed by image analysis applications. This book comprehensively explores application-specific enhancement and reconstruction techniques including satellite image enhancement, face hallucination, low-resolution face recognition, medical image enhancement and reconstruction, reconstruction of underwater images, text image enhancement, biometrics, etc. Chapters will present a detailed discussion of the challenges faced in handling each particular kind of image, analysis of the best available solutions, and an exploration of applications and future directions. The book provides readers with a deep dive into denoising, dehazing, super-resolution, and use of soft computing across a range of engineering applications. Presents comprehensive coverage of digital image enhancement and reconstruction techniques Explores applications across range of fields, including intelligent surveillance systems, human-computer interaction, healthcare, agriculture, biometrics, modelling Explores different challenges and issues related to the implementation of various techniques for different types of images, including denoising, dehazing, super-resolution, and use of soft computing