Digital Image Processing: A Practical approach for Image Restoration

Milindkumar Sarode

ISBN 10: 6202557664 / ISBN 13: 9786202557665

Published by LAP LAMBERT Academic Publishing Okt 2020, 2020

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Various filtering algorithms have been discussed in this book to suppress not only Gaussian noise, Impulse noise and Mixture of Gaussian and Impulse noise, but also Rician and Impulse noise. The filter performance is compared with existing filters in terms of Peak-Signal-to-Noise ratio (PSNR) and visual quality of restored images. The entire discussion is focused on developing an efficient image denoising filters such as Universal noise removal algorithm, Edge Preservation and Removal of Cracks and noise, Rician and Gaussian noise reduction in 3-D MRI, Rician and Speckle noise reduction in Ultrasound images, Total Variation Restoration algorithm, Restoration of images due to Camera Motion blur, Fusion and Denoising of more than two Multifocus images, Motion Compensation in MRI, 3-D Reconstruction of coronary arteries, Fuzzy System for Color image enhancement, Adaptive Bilateral Filter for sharpness, enhancement and noise removal, and Restoration of Video sequences. The performance of the filters are tested in terms of Peak-Signal-to-noise ratio (PSNR), Signal-to noise ratio (SNR), Mean absolute differences (MAE), Root mean square error (RMSE) and visual quality of restored images.Books on Demand GmbH, Überseering 33, 22297 Hamburg 264 pp. Englisch.

Seller Inventory # 9786202557665

Bibliographic Details

• Title: Digital Image Processing: A Practical approach for Image Restoration
• Publisher: LAP LAMBERT Academic Publishing Okt 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 6202557664
• ISBN 13: 9786202557665
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

Various filtering algorithms have been discussed in this book to suppress not only Gaussian noise, Impulse noise and Mixture of Gaussian and Impulse noise, but also Rician and Impulse noise. The filter performance is compared with existing filters in terms of Peak-Signal-to-Noise ratio (PSNR) and visual quality of restored images. The entire discussion is focused on developing an efficient image denoising filters such as Universal noise removal algorithm, Edge Preservation and Removal of Cracks and noise, Rician and Gaussian noise reduction in 3-D MRI, Rician and Speckle noise reduction in Ultrasound images, Total Variation Restoration algorithm, Restoration of images due to Camera Motion blur, Fusion and Denoising of more than two Multifocus images, Motion Compensation in MRI, 3-D Reconstruction of coronary arteries, Fuzzy System for Color image enhancement, Adaptive Bilateral Filter for sharpness, enhancement and noise removal, and Restoration of Video sequences. The performance of the filters are tested in terms of Peak-Signal-to-noise ratio (PSNR), Signal-to noise ratio (SNR), Mean absolute differences (MAE), Root mean square error (RMSE) and visual quality of restored images.