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Table 4 Image denoising on nature images with NLM ,BM3D, OGLR, ADNet and our method: performance comparisons in PSNR (Left, in dB) and SSIM (Right)

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

Images noise NLM BM3D OGLR ADNet Our method
house \({\sigma }=10\) 37.55dB 0.9504 36.71dB 0.9212 38.88dB 0.9622 36.57dB 0.9077 38.50dB 0.9533
\({\sigma }=20\) 33.86dB 0.9146 33.77dB 0.8721 35.87dB 0.9405 34.12dB 0.8713 35.68dB 0.9448
\({\sigma }=30\) 31.01dB 0.8659 32.09dB 0.8473 33.86dB 0.9183 32.62dB 0.8556 33.79dB 0.9279
\({\sigma }=40\) 29.03dB 0.8113 30.65dB 0.8249 32.49dB 0.9024 31.26dB 0.8387 31.33dB 0.9085
\({\sigma }=50\) 27.61dB 0.7559 29.69dB 0.8116 30.67dB 0.8631 30.28dB 0.8230 31.13dB 0.8969
church \({\sigma }=10\) 37.70dB 0.9600 39.53dB 0.9670 39.59dB 0.9744 40.38dB 0.9710 39.18dB 0.9629
\({\sigma }=20\) 33.35dB 0.9151 35.99dB 0.9455 36.04dB 0.9527 37.23dB 0.9580 35.68dB 0.9564
\({\sigma }=30\) 30.54dB 0.8591 33.92dB 0.9254 33.71dB 0.9292 35.06dB 0.9413 33.59dB 0.9378
\({\sigma }=40\) 28.84dB 0.8021 32.42dB 0.9056 32.13dB 0.9048 33.64dB 0.9398 30.80dB 0.9101
\({\sigma }=50\) 27.63dB 0.7463 31.33dB 0.8974 30.60dB 0.8636 32.37dB 0.9142 30.66dB 0.8945
flower \({\sigma }=10\) 35.69dB 0.9469 38.15dB 0.9667 37.77dB 0.9655 38.70dB 0.9712 37.78dB 0.9620
\({\sigma }=20\) 32.40dB 0.8986 34.29dB 0.9314 34.33dB 0.9342 35.22dB 0.9476 34.15dB 0.9362
\({\sigma }=30\) 30.26dB 0.8497 32.19dB 0.8988 31.95dB 0.8998 33.17dB 0.9225 31.94dB 0.9049
\({\sigma }=40\) 28.90dB 0.8033 30.67dB 0.8679 30.51dB 0.8713 31.78dB 0.9046 29.73dB 0.8661
\({\sigma }=50\) 27.90dB 0.7575 29.72dB 0.8529 29.09dB 0.8231 30.44dB 0.8813 29.37dB 0.8544
jar \({\sigma }=10\) 35.71dB 0.8954 38.63dB 0.9459 37.91dB 0.9354 37.52dB 0.9349 38.01dB 0.9333
\({\sigma }=20\) 33.33dB 0.8471 35.26dB 0.8986 34.96dB 0.8911 34.58dB 0.8887 34.93dB 0.8884
\({\sigma }=30\) 31.63dB 0.7992 33.41dB 0.8637 33.29dB 0.8598 32.96dB 0.8506 33.40dB 0.8615
\({\sigma }=40\) 30.43dB 0.7534 32.04dB 0.8350 32.09dB 0.8375 32.02dB 0.8222 32.02dB 0.8403
\({\sigma }=50\) 29.48 0.7083 31.17dB 0.8191 31.00dB 0.8128 31.13dB 0.8002 31.08dB 0.8204
bird \({\sigma }=10\) 35.80dB 0.9735 38.28dB 0.9829 37.04dB 0.9796 38.55dB 0.9754 36.41dB 0.9679
\({\sigma }=20\) 32.42dB 0.9385 34.30dB 0.9651 33.62dB 0.9621 35.20dB 0.9615 32.79dB 0.9635
\({\sigma }=30\) 29.66dB 0.8928 31.99dB 0.9499 31.58dB 0.9421 33.21dB 0.9506 31.38dB 0.9503
\({\sigma }=40\) 27.66dB 0.8407 31.63dB 0.9377 30.01dB 0.9201 31.63dB 0.9377 28.47dB 0.9261
\({\sigma }=50\) 26.16dB 0.7851 28.95dB 0.9101 28.62dB 0.8830 30.62dB 0.9189 29.02dB 0.9213
  1. Bold and underline to mark the best and the second best results for each quality index, respectively