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Table 4 Comparison of the PSNR (in dB) for inpainting of images with various corruption levels based on analytic dictionaries, DCT, MCA, and dictionaries learned on all available corrupted image patches, BPFA, BPFAomp, wKSVD, and ITKrMM (modified inpainting is marked with a *)

From: Fast dictionary learning from incomplete data

  Algorithm Bar. Cam. Hou. Man. Pepp. Pir.
30% corruption Noisy Im. 11.17 10.81 10.11 10.82 11.18 11.70
  DCT64 37.49 32.66 41.89 30.60 39.12 35.40
  DCT144 37.08 32.41 41.49 30.86 38.90 35.42
  MCA 35.89 32.45 39.62 28.38 35.59 33.35
  BPFA 34.76 32.08 39.76 29.58 37.92 34.38
  BPFAomp 35.36 32.23 41.09 30.81 38.66 35.42
  wKSVD 35.87 32.62 41.42 30.41 38.64 35.09
  ITKrMM1 36.12 32.80 41.97 30.85 39.20 35.60
  ITKrMM3 37.16 3 3 . 0 4 4 2 . 3 0 3 0 . 9 2 3 9 . 8 0 3 6 . 0 8
50% corruption Noisy Im. 8.95 8.59 7.88 8.60 8.96 9.47
  DCT64 32.72 28.56 36.65 26.99 34.01 31.10
  DCT144 32.46 28.46 36.40 27.25 33.93 31.17
  MCA 32.50 28.99 36.54 25.34 32.35 29.86
  BPFA 32.97 28.89 37.71 27.25 35.29 31.89
  BPFAomp 32.98 28.87 37.88 27.29 35.41 32.18
  wKSVD 33.23 2 9 . 5 5 3 8 . 2 1 27.79 3 5 . 4 1 32.12
  ITKrMM1 33.28 29.44 37.75 27.96 35.31 32.14
  ITKrMM3 3 3 . 8 2 29.48 38.04 2 7 . 9 7 35.30 3 2 . 2 6
70% corruption Noisy Im. 7.48 7.13 6.42 7.13 7.50 8.01
  DCT64 28.21 24.86 31.49 24.29 29.05 27.21
  DCT144 28.09 24.81 31.37 24.44 28.81 27.32
  MCA 28.74 25.71 33.42 23.29 28.56 26.55
  BPFA 29.40 25.74 33.56 24.93 3 1 . 4 3 28.77
  BPFAomp 29.22 25.61 33.05 25.10 31.12 28.63
  BPFAomp* 29.23 25.60 33.04 25.11 31.18 28.74
  wKSVD 29.70 25.89 33.96 25.09 31.17 28.76
  wKSVD* 29.74 26.02 3 4 . 0 9 25.09 31.32 2 8 . 8 4
  ITKrMM1 29.48 25.84 33.26 25.11 29.64 28.53
  ITKrMM1* 2 9 . 9 3 2 6 . 3 4 33.65 2 5 . 1 2 31.26 28.83
  1. The results are averaged over 5 random masks and initialisations. The best result for each setting is marked in bold