EURASIP Journal on Advances in Signal Processing

Table 2 The learned \(\{\beta _k\}\) for real natural images with different levels of noise

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

	\(\beta _0\)	\(\beta _1\)	\(\beta _2\)	\(\beta _3\)	\(\beta _4\)
\(\sigma <5\)	1.0058	0.0002	0.0003	− 0.0003	0.0052
\(\sigma =10\)	0.9996	0.0003	− 0.0001	− 0.0003	0.0069
\(\sigma =20\)	0.9946	0.0001	0.0001	0.0001	0.0046
\(\sigma =30\)	0.9940	0.0000	0.0001	0.0001	0.0070
\(\sigma =40\)	0.9955	− 0.0004	− 0.0003	− 0.0008	0.0153
\(\sigma =50\)	0.9903	− 0.0012	− 0.0016	− 0.0016	0.0085

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