Compressed Sensing (Paperback)

Jonathon M. Sheppard

ISBN 10: 1536130826 / ISBN 13: 9781536130829

Published by Nova Science Publishers Inc, New York, 2018

About this Item

Paperback. In this compilation, the authors comprehensively investigate SOD effects in the homeostasis of mammal endometrium, using available information on several species and their team experience relating to the topic. In addition, they address its role in endometrial integrity and some uterine clinical conditions and infertility. The current knowledge of plant SODs, their abiotic-stress modulated expression and activity, and analyses results on genetic engineering of plant SODs are summarized. Significance of superoxide dismutases in the crop improvement for stress tolerance is also discussed. This book reviews the oxidative stress and damage to plants, while also summarizing the characteristics of SOD enzymes and discussing their involvement in the tolerance of plants against abiotic stress. Additionally, many authors have studied the protective role of SOD in the mice cochlea, however more recently the role of SOD gene polymorphisms in the susceptibility of sudden sensorineural hearing loss has been investigated. Therefore, the authors also examine the role of SOD in the cochlea and its involvement in the pathogenesis of noise induced hearing loss, age related hearing loss and sudden sensorineural hearing loss. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781536130829

Bibliographic Details

• Title: Compressed Sensing (Paperback)
• Publisher: Nova Science Publishers Inc, New York
• Publication Date: 2018
• Language: English
• ISBN 10: 1536130826
• ISBN 13: 9781536130829
• Binding: Paperback
• Condition: new

About this title

Synopsis

Compressed Sensing: Methods, Theory and Applications presents and describes a method of image Arbitrary Sampling and Bounded Spectrum Reconstruction (ASBSR-method) that allows for drawing near the image sampling rate theoretical minimum. This compilationalso discusses results of experimental verification of the ASBSR-method and its possible applicability extensions to solving various underdetermined inverse problems such as: color image demosaicing, image in-painting, image reconstruction from their sparsely sampled or decimated projections, image reconstruction from the modulus of its Fourier spectrum, and image reconstruction from its sparse samples in Fourier domain. Following this, the authors examine a novel framework to obtain HR images from CS imaging systems capturing multiple Low Resolution (LR) images of the same scene. The assumption that when an image admits a sparse representation in a transformed domain, a blurred version of it will also be sparse in the transformed domain, allows for the recovery of blurred images from CS observations. This proposed Compressed Sensing Super Resolution (CSSR) approach, combines existing CS reconstruction algorithms with an LR to HR approach based on the use of a new robust sparsity promoting prior based onsuper Gaussian regularisation. Additionally, several image CS recovery methods are studied. The concept of CS in image processing is introduced and a brief description of an effective CS reconstruction algorithm, called block-based CS with smoothed-projected Landweber reconstruction (BCS-SPL), is presented. Next, an adaptive CS method is presented, which provides a higher recovered image quality with respect to the BCS-SPL algorithm--