Technology Research, Based On The Regularization Super-resolution Image Sequence Reconstruction

In the current field of application for digital image, there often exists the optical blur, motion blur, down-sampling and noise in the process of imaging due to being affected by these factors like the physical conditions of imaging systems, climatic conditions and so on. It makes the image actually obtained be degraded, and leads to resolution of the image not to meet the application requirements. The idea of technology of super resolution reconstruction for image sequence is to use low resolution image sequence to reconstruct a high resolution image, which can exactly resolve the problem of existing in current field of application for digital image. However, the function of super resolution reconstruction for image sequence is ill-posed, and still exist defects in practical applications.Since regularization approach is an effective way to solve ill-posed problem, this paper completely analyzed and researched the regularization method based on super resolution reconstruction for image sequence in order to resolve the problems mentioned above. The main research works are as follows:First, a new adaptive regularization based on super resolution reconstruction for image sequence was proposed. The registration parameters estimated were assumed accurate in traditional methods, but in practice, error existed in motion estimation to a certain extent. On the basis of traditional maximum a posteriori estimation model of super resolution reconstruction for image, the impact of estimation error on reconstruction were fully considered. Regularization parameters were selected by using adaptive technique. The objective function integrated the advantages of several models was designed, and the model of super resolution reconstruction for image sequence based on regularization was improved. The simulation experiment showed that the proposed method made the reconstructed image take on better visual effect.Second, the algorithm of super resolution reconstruction for image sequence based on L2 norm of total variation regularization was improved. The relative contributions of each frame low resolution image to reconstruction were assumed equal in the traditional algorithm of super resolution reconstruction for image sequence based on regularization, which made the result of reconstruction be influenced. The relative contributions of each frame low resolution image to reconstruction were fully considered, and total variation regularization method was used in the super resolution reconstruction for image sequence; it was used to overcome the ill-posed problem of image reconstruction, and the edge of image was effectively preserved. The simulation experiment showed that the improved algorithm not only enhanced the ability of preserving image edge, but also effectively restrained the noise, which made the reconstructed image clearer.Third, the algorithm of fast super resolution reconstruction for image sequence based on total variation regularization was proposed. The data term in traditional objective function of total variation regularization was in the form of L2 norm, but L2 norm was more complicated. The traditional objective function of total variation regularization was improved by using L1 norm instead of L2 norm. The simulation experiment showed that the speed of reconstruction for the proposed algorithm comparing to the traditional algorithm was faster, and the result of reconstruction was similar, or even better.