Multichannel Image Deconvolution - Javed, Umer
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Blind image deconvolution is a very challenging and important research field. It has been proved to be useful in many application areas such as medical imaging, astronomical imaging and remote sensing. Several methods have been used for single channel framework, but in this book a solution to Multichannel Blind Image Deconvolutioin problem has been proposed when the original image is assumed to be sparse and limited knowledge of the Point Spread Function (PSF) is available. The sparse nature of image has leaded us to consider the case of Magnetic Resonance Force Microscopy (MRFM), hence PSF of…mehr

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Produktbeschreibung
Blind image deconvolution is a very challenging and important research field. It has been proved to be useful in many application areas such as medical imaging, astronomical imaging and remote sensing. Several methods have been used for single channel framework, but in this book a solution to Multichannel Blind Image Deconvolutioin problem has been proposed when the original image is assumed to be sparse and limited knowledge of the Point Spread Function (PSF) is available. The sparse nature of image has leaded us to consider the case of Magnetic Resonance Force Microscopy (MRFM), hence PSF of MRFM machine is used in this project. An alternating minimization algorithm is used for the purpose of restoring the original image as well as the P.S.F. The proposed approach has successfully restored the original image and system's PSF.
Autorenporträt
Umer Javed has done Masters in Electronic Engineering with specialization in Signal & Image processing from International Islamic University Islamabad in 2009. His research interests includes Evolutionary computing techniques, Neural Networks, Fuzzy Logic and Genetic Programming. His current research is focused on Unsupervised Machine Learning.