Abstract
This paper presents an efficient method for video super-resolution (SR) based on two main ideals: Firstly, input video frames can be separated into two components, nontexturing image and texturing image. Then each component image is applied to a compatible interpolation method to improve the quality of high-resolution (HR) reconstructed frame. Secondly, based on the approach that border regions of image details are the most lossy information regions from the sampling process. Therefore, a task of compensation interpolation is essential to increase the quality of the reconstructed HR images. From these discussions, we proposed an efficient method for video SR by combining the spatial interpolation in different texturing regions and the sampling compensation interpolation to improve the quality of video super-resolution. Our results shown that, the quality of HR frames, reconstructed by the proposed method, is better than that of other methods, , and in recently. The significant contribution is the low complexity of the proposed method. Hence, it is possible to apply the proposed algorithm to real-time video super-resolution applications.
Highlights
Video super-resolution is to reconstruct and create HR video frames from the input lowresolution (LR) video frames
In order to decrease the degradation and increase the quality of the reconstructed HR frame, this paper presents an efficient method for single-frame video SR based on two main ideas: Firstly, input video frames are separated into two components, low-frequency image and high frequency image
We proposed an efficient method for video SR by combining the spatial interpolation in different frequency domains and the sampling compensation interpolation for improving the quality of SR video images
Summary
Video super-resolution is to reconstruct and create HR video frames from the input lowresolution (LR) video frames. With the aim of our study for video superresolution, we have to solve the two key problems by decreasing the degradation at the ledge of texturing details and directing to realtime processing for the proposed SR algorithm. In order to decrease the degradation and increase the quality of the reconstructed HR frame, this paper presents an efficient method for single-frame video SR based on two main ideas: Firstly, input video frames are separated into two components, low-frequency image and high frequency image. A task of compensation interpolation is essential to increase the quality of reconstructed HR image Based on these ideas, we proposed an efficient method for video SR by combining the spatial interpolation in different frequency domains and the sampling compensation interpolation for improving the quality of SR video images. Based on the parameters we can define the Pchip interpolation function of
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