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Video super resolution method for self-adaption-based superpixel-oriented autoregression model

A superpixel-oriented, autoregressive model technology, applied in the field of computer vision, can solve problems such as image quality needs to be improved, and video cannot be applied

Inactive Publication Date: 2013-11-20
北京超放信息技术有限公司
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AI Technical Summary

Problems solved by technology

However, the image quality of the result obtained by this method needs to be improved, and it cannot be applied to many realistic videos due to the dependence on the original high-resolution key frames in the video.

Method used

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  • Video super resolution method for self-adaption-based superpixel-oriented autoregression model
  • Video super resolution method for self-adaption-based superpixel-oriented autoregression model
  • Video super resolution method for self-adaption-based superpixel-oriented autoregression model

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Embodiment Construction

[0066] The present invention converts super-resolution of low-resolution video into image super-resolution for key frames and non-key frames respectively, wherein the sparse regression method for super-resolution key frames takes less time and has good results, taking video frames into consideration at the same time The spatio-temporal characteristics of non-key frames are super-resolution based on an adaptive superpixel-guided autoregressive model, and finally the super-resolution of video is realized. The obtained results are characterized by rich texture details and higher resolution.

[0067] The video super-resolution method based on the adaptive superpixel-guided autoregressive model of the present invention is characterized in that: video frames are divided into key frames and non-key frames, and a method based on sparse regression and natural image pairs is used for the key frames. The method performs super-resolution, and adopts a method based on an adaptive superpixe...

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Abstract

The invention belongs to the technical field of computer vision. In order to provide a video super resolution acquisition method which can be widely applied and can be used for obtaining high-quality videos, the technical scheme adopted by the invention is that a video super resolution method for a self-adaption-based superpixel-oriented autoregression model is characterized by comprising the following steps of dividing a video frame into key frames and non-key frames; performing super resolution on the key frames by adopting a sparse regression and natural image pair-based method, performing the super resolution on the non-key frames by adopting the video super resolution method for the self-adaption-based superpixel-oriented autoregression model by combining adjacent nearest key frames, and synthesizing the obtained super-resolution key frames and super-resolution non-key frames into a super-resolution video. The video super resolution method provided by the invention is mainly used for video processing.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to GPU-based optical flow theory, autoregressive model theory, superpixel theory, Markov random field theory and a method for realizing image super-resolution of a single image. Video super-resolution methods for superpixel-guided autoregressive models. Background technique [0002] For a long time, the restoration of high-resolution video through low-resolution video has made important breakthroughs in key technologies, has become mature and has been widely used in many fields such as surveillance video, Internet video, digital TV, and intelligent transportation. However, the traditional method of super-resolution video needs high-resolution key frames in the video, and the obtained super-resolution results have problems such as blockiness or noise. The video super-resolution method based on an adaptive superpixel-guided autoregressive model can achieve higher resolutions, a...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T3/40
Inventor 李坤江健民朱彦铭杨敬钰
Owner 北京超放信息技术有限公司
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