Real-time Image Fusion Processing for Astronomical Images

This thesis provides a detailed study of ten different methods of Image fusion techniques and their theoretical background and their step-by-step detailed algorithmic implementation. Implementations of these techniques were applied to astronomical images to further study the results using MATLAB. The MATLAB code created for Astronomical Image fusion is provided for quick implementation and validation of the results.;After figuring out the best algorithms in MATLAB that gives either best image quality results or best execution time, the paper implement four algorithms using Python with the aim to continue further study with parallel and cloud computing. These algorithms are Principal Component Analysis (PCA), Average method, Discrete Wavelet Transform (DWT) using Haar filter and Discrete Wavelet Transform (DWT) using Daubechies filter.;The work then provides an introductory study of parallel computing, major concepts in their implementation and discusses issues facing the parallel implementation. Then, a parallel computing implementation of the Python code for Astronomical Image fusion is applied using two methods. A default Python library and an external Python library named JobLib and discussion, and comparison of the results versus the sequential computing is provided. The Python code for sequential and parallel computing is provided for quick implementation and validation of the results.;The paper then explores the emerging field of cloud computing and its advantages over regular processing. Then, an implementation of the Python code for Astronomical Image fusion is applied using Amazon Web service's (AWS) cloud computing service Amazon Elastic Compute Cloud (EC2) with two different systems a Windows-based system and a Linux based system and then discussion and comparison of the results versus the sequential computing is provided. The Python code for the cloud computing is provided for quick implementation and validation of the results.;Then this paper compares the results of the ten different methods of image fusion done over astronomical images. The comparison is made using execution time of each algorithm and thirteen different ways of image quality and image fusions effectiveness with regards to astronomical images. The thirteen measures of effectiveness are studied, and their theoretical background and their step-by-step detailed algorithmic implementation are provided. The MATLAB code created for the thirteen measures of effectiveness is provided for quick implementation and validation of the results.;Finally in this thesis, a method is provided that compares two astronomical images and returns a probability if these images came from the same source by extracting the fixed error signal produced by the Charge-coupled device (CCD) camera which would be unique to each camera and then compare it. MATLAB code has been provided for quick implementation and validation of the proposed algorithm.