Open AccessReconstruction of RGB composite CT lung image by blind de-convolution for various blurring functions
Author(s) -
Kalpana Vattikunta,
Rajini G.K
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.8.9448
Subject(s) - image restoration , artificial intelligence , computer vision , convolution (computer science) , mathematics , regularization (linguistics) , composite image filter , computer science , filter (signal processing) , image (mathematics) , rgb color model , iterative reconstruction , image processing , artificial neural network
A competent and flexible tool to optimize inverse problems related to image reconstruction by restoration is Alternating Direction Method of Multipliers (ADMM) with the knowledge of known blur. This method is later modified to perform Blind Image De-blurring (BID) of unknown blur on original image by using some function of regularization. But, in real world for de-blurring, the prior knowledge of blurring filter is important. In this research work, estimates of the image and blurring operator are obtained by considering significant image edges. An ADMM iteration criterion forms the base for which whiteness measurement parameter estimation which includes auto-correlation, auto-covariance. Using these parameters best ISNR is taken as input resulting from the iterations. Different degradation conditions are considered in the analysis to estimate the performance and to bring a conclusion to the degradation and restoration pair by processing composite and component images of the input RGB-CT lung image.