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In this study the algorithm is applied to topographic data (synthetic landscapes, SRTM, TOPSAR) and the results are compared against using a mean filter, using LiDAR data as ground truth for the natural datasets.
By using a mean filter, the derivative curve is smoothed.
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Data were recorded at 1000 samples per second after amplification (bandwidth of 10 500 Hz), were rectified and smoothed through a 3 Hz second-order, zero phase, low-pass Butterworth filter, and were down-sampled to 40 samples per second using a mean filtering procedure.
Two-hundred and nineteen RCA-CVVH circuits were used with a mean filter life of 57.1 ± 41.7 hours (median 47, IQR 24 to 83).
Hybridizations were generally performed as duplicates and outlier data points from four spots (duplicate spots from duplicate chips) were filtered out using a mean ± 1.2 times standard deviation cut-off.
So the simplest way we used is a mean filtering.
The accuracy experiment proves the system has a precise measurement performance and the robustness experiment demonstrates the system using a truncated mean filter is reliable and robust.
Spatial context integration by the use of a mean filtering algorithm on explanatory variables increased the Kappa index on the extrapolated area by more than ten points.
A mean filter is used to estimate the center-pixel value in smooth region; in edged regions, the center pixel is predicted along the edge.
In the smooth region, a mean filter is used to estimate the center pixel from the eight-neighboring pixels; in the horizontal and vertical edge regions, the center-pixel value is predicted along the edge as the same as CAI4.
This noise subtraction accounted for the baseline spectral intensity of the FT. The final PT-OCT signal was filtered in the axial direction using a trimmed inner mean filter to reduce noise, thresholded using the intensity image to remove noise dominated PT-OCT pixels, and median filtered with a 4 pixel square kernel to reduce speckle noise.
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