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Table 2 PSNR and SSIM values obtained by five methods for seven different images degraded by kernel 2

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

ImageSizeMeasureBlurredMethod
    RLWienerTVBTVMAP- H1
Lena256×256PSNR23.1426.5226.4526.9126.9427.88
  SSIM0.67940.77200.78240.79430.79670.8410
Cameraman512×512PSNR24.3831.2430.7630.2930.3831.58
  SSIM0.77370.89590.89870.88950.89100.9153
House256×256PSNR24.7428.9328.6029.1829.2630.73
  SSIM0.72640.78610.78600.79820.80110.8469
Couple512×512PSNR23.6026.8326.9726.7526.7727.44
  SSIM0.56250.73410.74310.72920.72930.7653
Pirate512×512PSNR24.6728.1028.0628.0928.1628.83
  SSIM0.62820.77780.78520.77390.77660.8068
Boat512×512PSNR23.6327.3127.3927.0827.1027.81
  SSIM0.59390.75460.76230.74540.74570.7774
Fingerprint512×512PSNR18.0725.6425.9626.2326.3727.40
  SSIM0.39380.85680.86420.86600.86830.8959
  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