Super-Resolution For Compressed Video Sequence Reconstruction

In the field of image processing, the technology of super resolution reconstruction and image compression are popularly developed. In order to meet the increasing demand of reconstruction one or a sequence of high resolution image from compressed video, this paper focuses on following aspects:1) For working on the special application of super resolution technique on compressed video data reconstruction, we have studied the new questions introduce by compression algorithm and the theory of the muti-frame image super resolution. Considering degradation model can not be built exactly, we propose an effective spacial reconstruction framework of low complexity.2) In this paper, we have made some research on image registration algorithm in super resolution reconstruction. Since the accuracy of the registration algorithm results play an very important role in super resolution reconstruction, we propoded an algorithm that combines the optical flow method and Gaussian Pyramid method and achieve a motion estimation of subpixel accuracy.3) Since the track effect of the LK optical flow method can be badly affected by noise, this paper proposes a preprocessing algorithm, which is based on region detection, to weaken this affection, after studing the properties of the quantization noise in compressed video.4) In this paper, we study the basic theory and typic algorithms of the interpolation in super resolution reconstruction. And apply the kernel Regression theroy by Peyman Milanfar on interpolation algorithm of super resolution reconstruction by balancing the caculation complexcity and the effection. And this algorithm proposed can make good simulation of the data after registration.