Learning spatially-variable filters for super-resolution of text

Images magnified by standard methods display a degradation of detail that is particularly noticeable in the blurry edges of text. Current super-resolution algorithms address the lack of sharpness by filling in the image with probable details. These algorithms break the outlines of text. Our novel algorithm for super-resolution of text magnifies images in real-time by interpolation with a variable linear filter. The coefficients of the filter are determined nonlinearly from the neighborhood to which it is applied. We train the mapping that defines the coefficients 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.