Description
Book SynopsisThis 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.
Table of ContentsPart 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.
