The high computational complex of Super Resolution (SR) is a focused topic in many imaging applications, which involves to solve huge sparse linear systems. Solving such systems usually employs the iterative methods, such as Conjugate Gradient (CG). But in most variational Bayesian SR algorithms, CG method converges slowly with the coefficient matrix being ill-conditioned and takes long execution time. In this paper, we propose Preconditioned Conjugate Gradient (PCG) to solve the problem and analyze the performance of the different PCG solvers, Jacobi and incomplete Cholesky decomposition(IC). Experimental results demonstrate that the new method achieves accelerations compared with the traditional one while maintaining high visual quality of the reconstructed HR image, and, especially, the IC solver has a better performance.
According to analysis of runways geometric features in remote sensing images, a new airfield detection method
combining color, texture segmentation and shape analysis is presented, where the color and texture features are used for
global classification while shape information is used for local analysis. In order to extract airfield runway information, an
improved method based on direction and length filter is proposed, in which the useless scanning can be stopped promptly.
The experimental results presented in this paper show that this algorithm could eliminate the interference under complex
circumstance and could improve the efficiency and accuracy of military airfield recognizing and understanding. It has
higher computing speed and less space demand compared with the existing Hough-based algorithm. Furthermore, the
proposed algorithm is simple and easy to implement.