Total Variation (TV) l1 Norm Minimization Based Limited Data X-ray CT Image Reconstruction

Limited data CT Image reconstruction is a real-life problem. A Total Variation (TV) l1 norm minimization technique has been examined and validated here to reconstruct CT images from limited data incorporating a limited number of views along with limited angular span, a situation typical in engineering applications. The Lagrangian technique has been used to solve TV equations. The reconstructed CT image has been compared with the images reconstructed by SIRT, Higher Order TV (HOTV) technique, l2 norm minimization based technique and some other techniques with the help of various image quality index (IQI) parameters. The comparison shows that the proposed scheme is an attractive solution for limited data CT Image reconstruction from industrial and engineering perspective. The application of Sobolev space error analysis has also been given to ensure good global reconstruction.