Presents a review of image denoising algorithms with practical MATLAB implementation guidance Digital Image Denoising in MATLAB provides a comprehensive treatment of digital image denoising, containing a variety of techniques with applications in high-quality photo enhancement as well as multi-dimensional signal processing problems such as array signal processing, radar signal estimation and detection, and more. Offering systematic guidance on image denoising in theories and in practice through MATLAB, this hands-on guide includes practical examples, chapter summaries, analytical and programming problems, computer simulations, and source codes for all algorithms discussed in the book. The book explains denoising algorithms including linear and nonlinear filtering, Wiener filtering, spatially adaptive and multi-channel processing, transform and wavelet domains processing, singular value decomposition, and various low variance optimization and low rank processing techniques. Throughout the text, the authors address the theory, analysis, and implementation of the denoising algorithms to help readers solve their image processing problems and develop their own solutions. Explains how the quality of an image can be quantified in MATLAB Discusses what constitutes a “naturally looking” image in subjective and analytical terms Presents denoising techniques for a wide range of digital image processing applications Describes the use of denoising as a pre-processing tool for various signal processing applications or big data analysis Requires only a fundamental knowledge of digital signal processing Includes access to a companion website with source codes, exercises, and additional resources Digital Image Denoising in MATLAB is an excellent textbook for undergraduate courses in digital image processing, recognition, and statistical signal processing, and a highly useful reference for researchers and engineers working with digital images, digital video, and other applications requiring denoising techniques.

This book provides a comprehensive study in digital image interpolation with theoretical, analytical and Matlab® implementation. It includes all historically and practically important interpolation algorithms, accompanied with Matlab® source code on a website, which will assist readers to learn and understand the implementation details of each presented interpolation algorithm. Furthermore, sections in fundamental signal processing theories and image quality models are also included. The authors intend for the book to help readers develop a thorough consideration of the design of image interpolation algorithms and applications for their future research in the field of digital image processing. Introduces a wide range of traditional and advanced image interpolation methods concisely and provides thorough treatment of theoretical foundations Discusses in detail the assumptions and limitations of presented algorithms Investigates a variety of interpolation and implementation methods including transform domain, edge-directed, wavelet and scale-space, and fractal based methods Features simulation results for comparative analysis, summaries and computational and analytical exercises at the end of each chapter Digital Image Interpolation in Matlab® is an excellent guide for researchers and engineers working in digital imaging and digital video technologies. Graduate students studying digital image processing will also benefit from this practical reference text.

Presents a review of image denoising algorithms with practical MATLAB implementation guidance Digital Image Denoising in MATLAB provides a comprehensive treatment of digital image denoising, containing a variety of techniques with applications in high-quality photo enhancement as well as multi-dimensional signal processing problems such as array signal processing, radar signal estimation and detection, and more. Offering systematic guidance on image denoising in theories and in practice through MATLAB, this hands-on guide includes practical examples, chapter summaries, analytical and programming problems, computer simulations, and source codes for all algorithms discussed in the book. The book explains denoising algorithms including linear and nonlinear filtering, Wiener filtering, spatially adaptive and multi-channel processing, transform and wavelet domains processing, singular value decomposition, and various low variance optimization and low rank processing techniques. Throughout the text, the authors address the theory, analysis, and implementation of the denoising algorithms to help readers solve their image processing problems and develop their own solutions. Explains how the quality of an image can be quantified in MATLAB Discusses what constitutes a “naturally looking” image in subjective and analytical terms Presents denoising techniques for a wide range of digital image processing applications Describes the use of denoising as a pre-processing tool for various signal processing applications or big data analysis Requires only a fundamental knowledge of digital signal processing Includes access to a companion website with source codes, exercises, and additional resources Digital Image Denoising in MATLAB is an excellent textbook for undergraduate courses in digital image processing, recognition, and statistical signal processing, and a highly useful reference for researchers and engineers working with digital images, digital video, and other applications requiring denoising techniques.

This book introduces the fundamental concepts of modern digital image processing. It aims to help the students, scientists, and practitioners to understand the concepts through clear explanations, illustrations and examples. The discussion of the general concepts is supplemented with examples from applications and ready-to-use implementations of concepts in MATLAB®. Program code of some important concepts in programming language 'C' is provided. To explain the concepts, MATLAB® functions are used throughout the book. MATLAB® Version 9.3 (R2017b), Image Acquisition Toolbox Version 5.3 (R2017b), Image Processing Toolbox, Version 10.1 (R2017b) have been used to create the book material. Meant for students and practicing engineers, this book provides a clear, comprehensive and up-to-date introduction to Digital Image Processing in a pragmatic manner.

It is probably true quite generally that in the history of human thinking the most fruitful developments frequently take place at those points where two different lines of thought meet. Hence, if they actually meet, that is, if they are at least so much related to each other that a real interaction can take place, then one may hope that new and interesting developments may follow. Werner Heisenberg This volume contains papers presented at the August 1992 NATO Advanced Study Institute on Wavelets and Their Applications. The conference was held at the beautiful Il Ciocco resort near Lucca, in the glorious Tuscany region of northern Italy. Once again we gathered at this idyllic spot to explore and extend the reciprocity between mathematics and engineering. The dynamic interaction between world-renowned scientists from the usu ally disparate communities of pure mathematicians and applied scientists, which occurred at our 1989 and 1991 ASI's, continued at this meeting. Wavelet theory and technology is in an important growth stage at which theoretical and practical results are being compared with existing methods. There have been spectacular wavelet successes and sobering comparisons with traditional ideas-but still there is a wide expanse of scientific problems to explore. Since these problems lie at the forefront of both pure mathematics and applied science, our NATO ASI was especially pertinent at this time.

The book provides a comprehensive exposition of all major topics in digital signal processing (DSP). With numerous illustrative examples for easy understanding of the topics, it also includes MATLAB-based examples with codes in order to encourage the readers to become more confident of the fundamentals and to gain insights into DSP. Further, it presents real-world signal processing design problems using MATLAB and programmable DSP processors. In addition to problems that require analytical solutions, it discusses problems that require solutions using MATLAB at the end of each chapter. Divided into 13 chapters, it addresses many emerging topics, which are not typically found in advanced texts on DSP. It includes a chapter on adaptive digital filters used in the signal processing problems for faster acceptable results in the presence of changing environments and changing system requirements. Moreover, it offers an overview of wavelets, enabling readers to easily understand the basics and applications of this powerful mathematical tool for signal and image processing. The final chapter explores DSP processors, which is an area of growing interest for researchers. A valuable resource for undergraduate and graduate students, it can also be used for self-study by researchers, practicing engineers and scientists in electronics, communications, and computer engineering as well as for teaching one- to two-semester courses.

In the dynamic landscape of image processing, the pursuit of clarity and precision is unceasing. This book embarks on an exhaustive exploration of image enhancement, focusing on three pivotal domains: denoising, edge detection, and segmentation. These areas collectively form the cornerstone of image refinement, essential in applications ranging from medical diagnostics to artistic expression. The journey commences with a meticulous examination of Denoising Utilities, a multifaceted toolkit tailored for noise reduction. Techniques like wavelet denoising and adaptive filtering are dissected, providing readers with an extensive arsenal for image restoration. The incorporation of precise metrics ensures not only visual improvement but also quantifiable measures of enhancement. Edge Detection Utilities presents an array of algorithms designed to unveil crucial features within images. From the Sobel operator to the Gabor filter, each algorithm brings a unique perspective to the forefront. Beyond mere theoretical exposition, this section offers modified plotting utilities and seamless integration into the Main Program, enabling readers to wield these algorithms effectively. Segmentation Utilities usher readers into the realm of image partitioning, a process of dividing images into coherent regions. Techniques like Multi-Level Thresholding, K-Means Clustering, Watershed Algorithm, and Markov Random Fields (MRF) are explored. The inclusion of user-friendly forms and thoughtfully designed plotting utilities empowers readers to extract invaluable information from complex images. At the heart of this journey lies the Main Form, serving as the epicenter of operations. Its intuitive interface and seamless navigation pave the way for users to access a myriad of utilities, creating a cohesive and immersive experience. This form serves as the gateway to a world of image refinement and analysis. A critical component of image processing lies in visualizing the transformation. Plotting Utilities have been meticulously designed to offer dynamic visual representations of denoised, edge-detected, and segmented images. These tools bridge the gap between theoretical understanding and practical application. Understanding the effectiveness of denoising techniques is imperative. Wavelet Denoising Metrics provide a rigorous framework for quantifying the improvement achieved. These metrics offer insights into the impact of denoising on image quality, ensuring a scientifically grounded approach to enhancement. The efficacy of reaction-diffusion denoising techniques is assessed through specialized metrics. These metrics offer a quantitative assessment of the denoising process, enabling users to fine-tune parameters for optimal results. This section bridges theory with application, ensuring meaningful enhancements. Anisotropic diffusion denoising is evaluated using purpose-built metrics. These metrics provide a systematic evaluation of the denoising process, enabling users to make informed decisions regarding parameter selection. This section empowers users with the knowledge to achieve precise enhancements. The impact of spectral method denoising is quantified through dedicated metrics. These metrics offer a comprehensive assessment of the denoising process, enabling users to refine parameters for maximum effectiveness. This section ensures that enhancements are not only visually pleasing but also scientifically validated. This book, a compendium of practical knowledge and hands-on expertise, serves as a guide for both beginners and seasoned practitioners in the field of image processing. It aims to equip readers with not only an understanding of the intricacies of image enhancement but also the practical skills to wield this knowledge effectively. Through this journey, images cease to be mere representations; they become a source of profound insights, revealing hidden details and empowering users to extract meaningful information. So, let's embark on this illuminating voyage, where theory meets application, and images transform from pixels to a source of enlightenment.

The book includes new research results of scholars from the Second International Conference on Advanced Intelligent Technologies (ICAIT 2021) subtitled Intelligent Technology and Industry organized by IRNet International Academic Communication Center, held during October 15–17, 2021. The book covers research work from active researchers who are working on collaboration of industry and various intelligent technologies such as intelligent technologies applicable/applied to manufacturing and distribution of industrial products, factory automation, business, etc. The book focuses on theory, design, development, testing, and evaluation of all intelligent technologies applicable/applied to various parts of industry and its infrastructure. The topics included are all computational intelligence techniques applicable/applied to industry, intelligent techniques in data science applicable/applied to business and management, intelligent network systems applicable/applied to industrial production, intelligent technologies applicable to smart agriculture, and intelligent information systems for agriculture.

CSISE2011 is an integrated conference concentrating its focus upon Computer Science,Intelligent System and Environment. In the proceeding, you can learn much more knowledge about Computer Science, Intelligent System and Environment of researchers all around the world. The international conference will provide a forum for engineers, scientist, teachers and all researchers to discuss their latest research achievements and their future research plan. The main role of the proceeding is to be used as an exchange pillar for researchers who are working in the mentioned field. In order to meet high standard of Springer ́s Advances in Intelligent and Soft Computing ,the organization committee has made their efforts to do the following things. Firstly, poor quality paper has been refused after reviewing course by anonymous referee experts. Secondly, periodically review meetings have been held around the reviewers about five times for exchanging reviewing suggestions. Finally, the conference organization had several preliminary sessions before the conference. Through efforts of different people and departments, the conference will be successful and fruitful. We hope that you can get much more knowledges from our CSISE2011, and we also hope that you can give us good suggestions to improve our work in the future.