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23 February 2005 Content-adaptive neural filters for image interpolation using pixel classification
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With the advent of high-definition television, video phone, Internet and video on PCs, media content has to be displayed with different resolutions and high quality image interpolation techniques are increasingly demanded. Traditional image interpolation methods usually use a uniform interpolation filter on the entire image without any discrimination and they tend to produce some undesirable blurring effects in the interpolated image. Some content adaptive interpolation methods have been introduced to achieve a better performance on specific image structures. However, these content adaptive methods are limited to fitting image data into a linear model in each image structure. We propose extending the linear model to a flexible non-linear model, such as a multilayer feed-forward neural network. This results in a new interpolation algorithm using neural networks with coefficients based on the known pixel classification. Due to the fact that the number of classes using the pixel classification increases exponentially along with the filtering aperture size, we further introduce an efficient method to reduce the number of the classes. The results show that the proposed algorithm demonstrates more robust estimation in image interpolation and gives an additional improvement in the interpolated image quality. Furthermore, the work also shows that the use of pre-classification limits the complexity of the neural network while still achieving good results.
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Hao Hu, Paul M. Hofman, and Gerard de Haan "Content-adaptive neural filters for image interpolation using pixel classification", Proc. SPIE 5673, Applications of Neural Networks and Machine Learning in Image Processing IX, (23 February 2005);


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