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Table 3 PSNR and SSIM values obtained by five methods for seven different images degraded by kernel 3

From: Regularized supervised Bayesian approach for image deconvolution with regularization parameter estimation

ImageSizeMeasureBlurredMethod
    RLWienerTVBTVMAP- H1
Lena256×256PSNR21.5628.0228.3331.8532.1732.21
  SSIM0.60060.80490.82810.88920.89480.9070
Cameraman512×512PSNR23.1236.8036.4234.8334.9539.97
  SSIM0.78240.95130.94460.93620.93860.9698
House256×256PSNR24.7932.0332.0135.7435.9236.65
  SSIM0.76390.89190.89370.93670.93920.9510
Couple512×512PSNR23.6831.7132.5933.8933.7534.43
  SSIM0.61910.91090.92960.93870.93670.9475
Pirate512×512PSNR24.0433.0133.2634.3734.6836.06
  SSIM0.62170.91450.91970.92740.93080.9492
Boat512×512PSNR23.3231.0731.6733.1233.3034.33
  SSIM0.64080.89270.90260.91690.91740.9395
Fingerprint512×512PSNR17.5726.6726.7828.5528.5529.15
  SSIM0.39300.93520.93480.95580.95610.9580
  1. For each test setting, six results are provided: blurred, Richardson-Lucy algorithm, Wiener filter, TV method, BTV approach, and our proposed model. Bold format: the best score in each line