Video Superresolution Reconstruction Using Iterative Back Projection with Critical-Point Filters Based Image Matching

To improve the spatial resolution of reconstructed images/videos, this paper proposes a Superresolution (SR) reconstruction algorithm based on iterative back projection. In the proposed algorithm, image matching using critical-point filters (CPF) is employed to improve the accuracy of image registration. First, a sliding window is used to segment the video sequence. CPF based image matching is then performed between frames in the window to obtain pixel-level motion fields. Finally, high-resolution (HR) frames are reconstructed based on the motion fields using iterative back projection (IBP) algorithm. The CPF based registration algorithm can adapt to various types of motions in real video scenes. Experimental results demonstrate that, compared to optical flow based image matching with IBP algorithm, subjective quality improvement and an average PSNR score of 0.53 dB improvement are obtained by the proposed algorithm, when applied to video sequence

A Short Survey of Image Super Resolution Algorithms

Image super resolution is to estimate a high resolution image from a low resolution image or a sequence of low resolution images using image processing and machine learning technology. So far, there have emerged lots of super resolution algorithms. According to the input number of image, these algorithms can usually be divided as single image based algorithm and multiple images based algorithm. And according to technique principle, these algorithms can also be divided into three categories - interpolation based algorithm, reconstruction based algorithm and learning based one. This work mainly addresses the basic principle and different strategy of super resolution algorithms in detail. Then, the evaluation criteria and its application issues of super resolution are also discussed in the end