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In this paper, we proposed the detection-based orthogonal match pursuit (DOMP) algorithm which systematically provides the stop threshold based on the signal detection criteria.
Based on the empirical results of our previous work, we give a new method to select the stop threshold for the image reduction process, which reduces the total computation by half.
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In this study, an adaptive edge stopping threshold function given in Equation 8 is used.
The stopping threshold γ t shown in (17) can be calculated using numerical method [17].
However, correct implementation of this technique depends crucially on the choice of an edge stopping threshold.
Also, set the stopping threshold δ and the maximum number of iterations k max.
As mentioned earlier, existing techniques only used a single edge stopping threshold value for the whole image.
Moreover, according to the sub-optimal mechanisms described in Section 3.1, we use a stopping threshold A=24.5.
Every element in g x, y) is the edge stopping threshold value for the corresponding pixel in I e (x, y).
The smoothing effect varies between pixels, and the extent of this depends on the edge stopping threshold value in g x, y).
Equation 8 is defined as an edge stopping threshold matrix, and it has the same dimension as I e (x, y).
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