Abstract
AbstractA novel adaptive smoothing approach is proposed for image smoothing and discontinuities preservation. The method is based on a locally piecewise constant modeling of the image with an adaptive choice of a window around each pixel. The adaptive smoothing technique associates with each pixel the weighted sum of data points within the window. We describe a statistical method for choosing the optimal window size, in a manner that varies at each pixel, with an adaptive choice of weights for every pair of pixels in the window. We further investigate how the I-divergence could be used to stop the algorithm. It is worth noting the proposed technique is data-driven and fully adaptive. Simulation results show that our algorithm yields promising smoothing results on a variety of real images.KeywordsMean Square ErrorNoise VarianceImage DecompositionImage SmoothingAdaptive ChoiceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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