Skip to main content

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