IEEE Trans Image Process - Super-resolution without dense flow.

Tópicos

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{ motion(1329) object(1292) video(1091) }
{ can(774) often(719) complex(702) }
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Resumo

Super-resolution is a widely applied technique that improves the resolution of input images by software methods. Most conventional reconstruction-based super-resolution algorithms assume accurate dense optical flow fields between the input frames, and their performance degrades rapidly when the motion estimation result is not accurate enough. However, optical flow estimation is usually difficult, particularly when complicated motion is presented in real-world videos. In this paper, we explore a new way to solve this problem by using sparse feature point correspondences between the input images. The feature point correspondences, which are obtained by matching a set of feature points, are usually precise and much more robust than dense optical flow fields. This is because the feature points represent well-selected significant locations in the image, and performing matching on the feature point set is usually very accurate. In order to utilize the sparse correspondences in conventional super-resolution, we extract an adaptive support region with a reliable local flow field from each corresponding feature point pair. The normalized prior is also proposed to increase the visual consistency of the reconstructed result. Extensive experiments on real data were carried out, and results show that the proposed algorithm produces high-resolution images with better quality, particularly in the presence of large-scale or complicated motion fields.

Resumo Limpo

superresolut wide appli techniqu improv resolut input imag softwar method convent reconstructionbas superresolut algorithm assum accur dens optic flow field input frame perform degrad rapid motion estim result accur enough howev optic flow estim usual difficult particular complic motion present realworld video paper explor new way solv problem use spars featur point correspond input imag featur point correspond obtain match set featur point usual precis much robust dens optic flow field featur point repres wellselect signific locat imag perform match featur point set usual accur order util spars correspond convent superresolut extract adapt support region reliabl local flow field correspond featur point pair normal prior also propos increas visual consist reconstruct result extens experi real data carri result show propos algorithm produc highresolut imag better qualiti particular presenc largescal complic motion field

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