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