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Table 1 The learned \(\{\beta _k\}\) for depth images with different levels of noise

From: Optimal graph edge weights driven nlms with multi-layer residual compensation

  \(\beta _0\) \(\beta _1\) \(\beta _2\) \(\beta _3\) \(\beta _4\)
\(\sigma <5\) 1.0223 0.0002 0.0001 0.0001 0.0021
\(\sigma =10\) 0.9992 0.0000 − 0.0005 − 0.0001 0.0002
\(\sigma =20\) 0.9976 − 0.0003 − 0.0004 − 0.0004 − 0.0002
\(\sigma =30\) 0.9766 − 0.0006 − 0.0007 − 0.0009 − 0.0001
\(\sigma =40\) 0.9885 − 0.0012 − 0.0013 − 0.0019 0.0011
\(\sigma =50\) 0.9862 − 0.0021 − 0.0025 − 0.0032 − 0.0016