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Table 1 PSNR and SSIM values obtained by five methods for seven different images degraded by kernel 1

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

ImageSizeMeasureBlurredMethod
    RLWienerTVBTVMAP- H1
Lena256×256PSNR23.1530.7331.0132.7533.1733.95
  SSIM0.67310.84280.86470.91470.92020.9381
Cameraman512×512PSNR24.6634.9035.2236.2736.3737.67
  SSIM0.78240.92510.92490.95270.95460.9651
House256×256PSNR25.0931.9732.0035.5935.9436.13
  SSIM0.73790.85020.85690.92630.92920.9347
Couple512×512PSNR24.1733.4833.7734.1134.4934.89
  SSIM0.61270.91390.91580.93730.93970.9458
Pirate512×512PSNR24.7533.7533.5533.8034.2434.70
  SSIM0.62680.91140.91400.92310.92830.9365
Boat512×512PSNR23.9833.5733.6833.6033.9534.52
  SSIM0.61810.90440.90510.91510.91610.9276
Fingerprint512×512PSNR17.5426.8426.9828.5628.6629.27
  SSIM0.32920.91770.91890.94950.95070.9564
  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