Computational Intelligence Methods for Super-Resolution in Image Processing Applications

ISBN-13: 9783030679200 / Angielski / Twarda / 2021 / 305 str.

Anand Deshpande; Vania Vieira Estrela; Navid Razmjooy

Spis treści

Part I. A Panorama of Computational Intelligence in Super-Resolution Imaging.- Chapter 1. Introduction to Computational Intelligence and Super-Resolution.- Chapter 2. Review on Fuzzy Logic Systems with Super-Resolved Imaging and Metaheuristics for Medical Applications.- Chapter 3. Super-Resolution with Deep Learning Techniques-A Review.- Chapter 4. A Comprehensive Review of CAD Systems in Ultrasound and Elastography for Breast Cancer Diagnosis.- Part II. State-of-the-Art Computational Intelligence in Super-Resolution Imaging.- Chapter 5. Pictorial Image Synthesis from Text and Its Super-Resolution using Generative Adversarial Networks.- Chapter 6. Analysis of Lossy and Lossless Compression Algorithms for Computed Tomography Medical Images Based on Bat and Simulated Annealing Optimization Techniques.- Chapter 7. Super resolution-based Human-Computer Interaction System for Speech and Hearing Impaired using Real-Time Hand Gesture Recognition System.- Chapter 8. Lossy Compression of Noisy Images Using Autoencoders for Computer Vision Applications.- Chapter 9. Recognition of Handwritten Nandinagari Palm Leaf Manuscript Tex.- Chapter 10. Deep Image Prior and Structural Variation Based Super-Resolution Network for Fluorescein Fundus Angiography Images.- Chapter 11. Lightweight Spatial Geometric Models Assisting Shape Description and Retrieval and Relative Global Optimum Based Measure for Fusion.- Chapter 12. Dual-Tree Complex Wavelet Transform and Deep CNN-based Super-Resolution for Video Inpainting with Application to Object Removal and Error Concealment.- Chapter 13. Super-Resolution Imaging and Intelligent solution for Classification, Monitoring and Diagnosis of Alzheimer's Disease.- Chapter 14. Image Enhancement using Non-Local Prior and Gradient Residual Minimization for Improved Visualization of Deep Underwater Image.- Chapter 15. Relative Global Optimum Based Measure for Fusion Technique in Shearlet Transform Domain for Prognosis of Alzheimer Disease.

O autorze

Anand Deshpande is currently serving as the Principal and Director of the Angadi Institute of Technology and Management (AITM), India. He received his PhD in Electronics and Communication and a Master of Technology degree in Digital Communication and Networking from Visvesvaraya Technological University, and a Bachelor of Engineering degree in Electronics and Communication Engineering from Karnatak University, Dharwad. His research work has been published in international journals, international conferences, and books, and he has filed patents in several areas. Dr. Deshpande is a reviewer for a number of journals published by the IEEE, The Institution of Engineering and Technology (IET), and Springer. His research interests include artificial intelligence, image and video analytics, data analytics, and machine vision.

Vania Vieira Estrela is currently a member of the faculty in the Department of Telecommunications at Federal Fluminense University (UFF), Brazil. Professor Estrela obtained her BSc degree in Electrical and Computer Engineering (ECE) from Federal University of Rio de Janeiro (UFRJ), an MSc in ECE from the Technological Institute of Aeronautics (ITA) and Northwestern University, and her PhD in ECE from the Illinois Institute of Technology (IIT). She has taught at DeVry University, State University of Northern Rio de Janeiro (UENF), and the West Zone State University, Brazil. Her research interests include signal/image/video processing, inverse problems, computational and mathematical modeling, stochastic models, multimedia, electronic instrumentation, computational intelligence, automated vehicles, machine learning, and remote sensing. She is an Editor for the International Journal of Ambient Computing and Intelligence, International Journal on Computational Science & Applications, and the EURASIP Journal on Advances in Signal Processing, and a member of the IEEE and the Association for Computing Machinery (ACM). 

Navid Razmjooy holds a PhD in Electrical Engineering (Control and Automation) from Tafresh University, an MSc with honors in Mechatronics Engineering from the Isfahan Branch of Islamic Azad University (IAU), and a BSc from the Ardabil Branch of IAU. His research interests include renewable energies, control, interval analysis, optimization, image processing, machine vision, soft computing, data mining, evolutionary algorithms, and system control. He is a senior member of the IEEE and Young Researchers Club of IAU. Dr. Razmjooy has published five books and more than 120 papers in English and Farsi in peer-reviewed journals and conferences. He is a reviewer for several national and international journals and conferences.

 

Blurb

This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem ─ super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.

• Demystifies computational intelligence for those working outside of engineering and computer science;
• Introduces cross-disciplinary platforms and dialog;
  
• Emphasizes modularity for enhancing computational intelligence frameworks.