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Edge-preserving smoothers need not be taxed by a severe computational cost. We present, in this paper, a lean algorithm that is inspired by the bi-exponential filter and preserves its structure—a pair of one-tap recursions. By a careful but simple local adaptation of the filter weights to the data, we are able to design an edge-preserving smoother that has a very low memory and computational footprint...
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