Super-resolution Restoration Of Video Sequences

Super-resolution restoration of video sequences is useful and often critical in many existing applications. Some imaging systems employ detector arrays that are not sufficiently dense to meet the Nyquist criterion during image acquisition. Therefore, the spatial resolution afforded by the optics can't be fully utilized in such imaging systems. This paper researches and improves two important algorithms to reconstruct high resolution images ,with reduced aliasing, from a sequences of undersampled rotated and shifted frames. The reconstructed image resolution is from two to four times higher than the undersampled frames. In this paper a motion compensation iterative algorithm is designed base on the multiple input restoration algorithm for enhancing the resolution of monochrome and color image sequences. Various approaches toward motion estimation are investigated and compared. Improving the spatial resolution of an image sequences critically depends upon the accuracy of the motion estimator. Particular attention is paid to the use of the color channels in estimating the motion. The main premise is that three different intensity channels conveying the same motion information and yielding more accurate motion estimates. Experiments are performed on monochrome and color image sequences, and performance is measured by the peak signal to noise ratio(PSNR).In conjugate gradient optimization algorithm, the continuous and digital models of an imaging system are defined to explain image acquisition, the image registration algorithm and the conjugate gradient reconstruction algorithm are designed. Experiment results show that the conjugate gradient reconstruction algorithm is robust, rapid convergent, and memory saved. This algorithm is prone to be extended and make it possible that multiple sensor data fusion constructs high resolution images. Ji Xian-Cai[Application of computer] Directed by Su Kai-na...