Images magnified by standard methods display a degradation of detail, particularly noticeable in the blurry edges of text. Current super-resolution algorithms that address the lack of sharpness by filling in the image with probable details hallucinate broken outlines when applied to text. Our novel algorithm for super-resolution of text magnifies images in real-time by interpolation with a variable linear filter determined nonlinearly from the neighborhood to which it is applied. We train the mapping that defines the linear filter to specifically enhance edges of text, producing a conservative algorithm that infers the detail of magnified text. Possible applications include resizing web page layouts or other interfaces, and enhancing low resolution camera captures of text. In general, learning spatially-variable filters is applicable to other image filtering tasks.
This paper discusses a decision-tree approach to the problem of assigning probabilities to words following a given text. In contrast with previous decision-tree language model attempts, an algorithm for selecting nearly optimal questions is considered. The model is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the well-known trigram model.
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