A visual attention approach to image interpolation
ABSTRACT In this study, a new image interpolation approach using the visual attention model is proposed. First, the high-quality saliency map of an image to be interpolated is generated in an effective manner. Then based on the saliency map, the bilinear interpolation method and a proposed interpolation method are employed for non-saliency blocks and saliency blocks, respectively, to obtain the final interpolation results. Based on the experimental results obtained in this study, the interpolation results of the proposed method are better than the corresponding results of the three comparison methods.
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ABSTRACT: A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index terms: Visual attention, scene analysis, feature extraction, target detection, visual search. Pi I. Introduction Primates have a remarkable ability to interpret complex scenes in real time, despite the limited speed of the neuronal hardware available for such tasks. Intermediate and higher visual processes appear to select a subset of the available sensory information before further processing , most likely to reduce the complexity of scene analysis . This selection appears to be implemented in the ...03/1999;
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ABSTRACT: This letter presents two adaptive interpolation methods based on applying an inverse gradient to conventional bilinear and bicubic interpolation. In simulations, the proposed methods exhibited a better performance than conventional bilinear and bicubic methods, particularly in the edge regions. In addition, the proposed methods can be used irrespective of the magnification factor (MF) and easily implemented due to their simple structure.IEEE Signal Processing Letters 04/2004; · 1.67 Impact Factor
Conference Proceeding: Contrast-based image attention analysis by using fuzzy growing.Proceedings of the Eleventh ACM International Conference on Multimedia, Berkeley, CA, USA, November 2-8, 2003; 01/2003