Virtual resolution enhancement of scale invariant textured images using stochastic processes
Résumé
We present a new method of magnification for textured im- ages featuring scale invariance properties. The procedure pre- serves the visual aspect as well as the statistical properties of the initial image. An augmentation of information is per- formed by locally adding small scale details below the ini- tial pixel size. This is made possible thanks to a family of scale invariant stochastic processes, namely compound Pois- son cascades. This extrapolating procedure yields a poten- tially infinite number of magnified versions of an image. It allows for large magnification factors (virtually infinite) and is physically conservative: zooming out to the initial reso- lution yields the initial image back. This work is motivated by an application to images of the quiet Sun to quantitatively predict statistical and visual properties of images taken by a forthcoming high resolution telescope.
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