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\begin{latin}
\latintitle{{Enhancing Video Quality Using Super-Resolution Methods}}
\latinauthor{{Mahmood Amintoosi}}
\latindegree{Doctor of Philosophy}
\latinthesisdate{\latintoday}
\latinsupervisor{Dr. M. Fathy, Dr. N. Mozayani}
\latindepartment{{Computer Engineering}}
\latinuniversity{{Iran University of Science and Technology}}
\latincity{Tehran}
\makelatintitle
\begin{latinabstract}

Video Super-resolution algorithms reconstruct a high resolution video from a low resolution video. Recent advances in Super-Resolution techniques show trends towards model-based or example-based approaches. Having a low resolution (LR) video and a few high resolution(HR) images from a specific scene is frequently occurred.In this thesis a new method for video super-resolution problem, based on this hypothesis, which some high-resolution  images from the scene are available, is proposed. Instead of the previous example based methods, which do blocking, here the whole training image is mapped to each frame.

Mapping of HR image into video frames, preciese registration, dealing with moving objects and propoer fusion are the main subjects of this thesis.  Proper mapping of the HR image onto Low Resolution frames has been done using a combination of feature-based and area-based registration methods. After creating a suitable mask for handling moving objects, each LR frame is fused with the registered HR image.

For LR images registration, a new method based on SIFT key-points is proposed. With contributing a structural similarity error measurement onto the objective function of a famous area-based registration method, a preciese mapping has been achieved. A Super-Resolution reconstruction method, applies on each sliding window of low-resolution frames for producing a reconstructed image. Then the HR training image is mapped to the reconstructed image coordinates using a proper transformation model. Fusion of the this image  with the reconstruction result makes the final desired HR frame of the given LR frames. Repeating this process for other subsets of LR frames produces the final HR video. Dealing with moving objects has been done using matrix decomposition and propoer masks. Experimental results show the superiour performance of the proposed  approaches.

\latinkeywords{Video Enahancement, Super-Resolution, Image Registration, Fusion, Blending.}
\end{latinabstract}
\end{latin}
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