Multi-objective Optimization Algorithm In Ct And Smsar,

Noisy signal and incomplete data which are detrimental to resolution and image quality are two unavoidable problems in some imaging systems. The traditional transform algorithms based on 2-dimensional Fourier Transform are the most popularly adopted methods but unsatisfactory in these problems. Moreover, the fixed imaging procedure makes them have little room for improvement. It was for this reason that we attempted to seek a better method to substitute the traditional ones. Because of its adaptability, multi-objective optimization has been successfully used in many engineering and scientific problems. The purpose of our work was to investigate the application of multi-objective optimization in two imaging problems桟omputerized Tomography (CT) and Spotlight Mode Synthetic Aperture Radar Imaging (SMSAR).CT and SMSAR are similar in mathematics. First, we will build the Maximum Entropy Optimization Model. Then we will construct the SMSAR optimization model based on the CT optimization model. However, CT and SMSAR are different in principle and image property, so we will make some modification to the objective functions and restriction functions accordingly. Last, we will test the feasibility of our new method using computer simulation and test its superiority by comparing its reconstruction results with those of traditional methods.Our experiment shows that vector optimization is an effective method for suppressing noise and improving resolution. Therefore, it is valuable for reconstruction from noisy projection data in CT and reconstruction from finite data in SMSAR.