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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. List of contributors - p. xi 7. Preface - p. xvii 8. Acknowledgments - p. xix 9. Chapter 1. Video enhancement and super-resolution - p. 1 10. Chapter 2. On estimating uncertainty of fingerprint enhancement models - p. 29 11. Chapter 3. Hardware and software based methods for underwater image enhancement and restoration - p. 71 12. Chapter 4. Denoising and enhancement of medical images by statistically modeling wavelet coefficients - p. 95 13. Chapter 5. Medical image denoising using convolutional neural networks - p. 115 14. Chapter 6. Multimodal learning of social image representation - p. 139 15. Chapter 7. Underwater image enhancement: past, present, and future - p. 151 16. Chapter 8. A comparative analysis of image restoration techniques - p. 173 17. Chapter 9. Comprehensive survey of face super-resolution techniques - p. 213 18. Chapter 10. Fusion-based backlit image enhancement and analysis of results using contrast measure and SSIM - p. 235 19. Chapter 11. Recent techniques for hyperspectral image enhancement - p. 253 20. Chapter 12. Classification of COVID-19 and non-COVID-19 lung computed tomography images using machine learning - p. 269 21. Chapter 13. Brain tumor image segmentation using K-means and fuzzy C-means clustering - p. 293 22. Chapter 14. Multimodality medical image fusion in shearlet domain - p. 317 23. Chapter 15. IIITM Faces: an Indian face image database - p. 329 24. Index - p. 343
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. List of contributors - p. xi 7. Preface - p. xvii 8. Acknowledgments - p. xix 9. Chapter 1. Video enhancement and super-resolution - p. 1 10. Chapter 2. On estimating uncertainty of fingerprint enhancement models - p. 29 11. Chapter 3. Hardware and software based methods for underwater image enhancement and restoration - p. 71 12. Chapter 4. Denoising and enhancement of medical images by statistically modeling wavelet coefficients - p. 95 13. Chapter 5. Medical image denoising using convolutional neural networks - p. 115 14. Chapter 6. Multimodal learning of social image representation - p. 139 15. Chapter 7. Underwater image enhancement: past, present, and future - p. 151 16. Chapter 8. A comparative analysis of image restoration techniques - p. 173 17. Chapter 9. Comprehensive survey of face super-resolution techniques - p. 213 18. Chapter 10. Fusion-based backlit image enhancement and analysis of results using contrast measure and SSIM - p. 235 19. Chapter 11. Recent techniques for hyperspectral image enhancement - p. 253 20. Chapter 12. Classification of COVID-19 and non-COVID-19 lung computed tomography images using machine learning - p. 269 21. Chapter 13. Brain tumor image segmentation using K-means and fuzzy C-means clustering - p. 293 22. Chapter 14. Multimodality medical image fusion in shearlet domain - p. 317 23. Chapter 15. IIITM Faces: an Indian face image database - p. 329 24. Index - p. 343
