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Table 5 The results of image denoising on depth dataset

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

noise NLM BM3D OGLR ADNet Our Method
\({\sigma }=10\) 41.96dB 0.9797 45.20dB 0.9887 44.40dB 0.9859 43.63dB 0.9864 43.36dB 0.9779
\({\sigma }=20\) 37.42dB 0.9742 40.88dB 0.9749 40.47dB 0.9705 40.19dB 0.9746 39.46dB 0.9601
\({\sigma }=30\) 34.53dB 0.8983 38.13dB 0.9581 38.02dB 0.9522 37.80dB 0.9598 37.67dB 0.9543
\({\sigma }=40\) 32.53dB 0.8463 36.08dB 0.9389 36.36dB 0.9368 36.17dB 0.9487 36.62dB 0.9581
\({\sigma }=50\) 31.11dB 0.7915 35.16dB 0.9368 34.40dB 0.9046 34.84dB 0.9379 34.94dB 0.9445
  1. Bold and underline to mark the best and the second best results for each quality index, respectively