http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Digital image enhancement and reconstruction
1 online resource (xxi, 355 pages) : illustrations (some color).
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.
Video enhancement and super-resolution -- On estimating uncertainty of fingerprint enhancement models -- Hardware and software based methods for underwater image enhancement and restoration -- Denoising and enhancement of medical images by statistically modeling wavelet coefficients -- Medical image denoising using convolutional neural networks -- Multimodel learning of social image representation -- Underwater image enhancement : past, present, and future -- A comparative analysis of image restoration techniques -- Comprehensive survey of face super-resolution techniques -- Fusion-based backlit image enhancement and analysis of results using contrast measure and SSIM -- Recent techniques for hyperspectral image enhancement -- Classification of COVID-19 and non-COVID-19 lung computed tomography images using machine learning -- Brain tumor image segmentation using K-means and fuzzy C-means clustering -- Multimodality medical image fusion in shearlet domain -- IIITM faces : an Indian face image database.
edited by Shyam Singh Rajput, Nafis Uddin Khan, Amit Kumar Singh, Karm Veer Arya.
Includes bibliographical references and index.
Hybrid computational intelligence for pattern analysis and understanding series