Dmitriy Vatolin

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In this paper we propose a novel super-resolution algorithm based on motion compensation and edge-directed spatial interpolation succeeded by fusion via pixel classification. Two high-resolution images are constructed, the first by means of motion compensation and the second by means of edge-directed interpolation. The AdaBoost classifier is then used to(More)
This research aims to sufficiently increase the quality of visual-attention modeling to enable practical applications. We found that automatic models are significantly worse at predicting attention than even single-observer eye tracking. We propose a semiautomatic approach that requires eye tracking of only one observer and is based on time consistency of(More)
In this paper, we propose a super-resolution algorithm based on image fusion via pixel classification. Two high-resolution images are constructed, the first by means of motion compensation and the second by means of image interpolation. The AdaBoost classifier is then used in the fusion of these images, resulting in an high-resolution frame. Experimental(More)
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