Research Article
Image Denoising of Adaptive Fractional Operator Based on Atangana–Baleanu Derivatives
Table 4
Comparison of the effectiveness of different fractional operators for the Goldhill image.
| | Noisy image | Method in [38] | TA_ABC | Ada_TA_ABC | GL_ABC | Ada_GL_ABC |
| σ = 15 | PSNR | 24.6053 | 25.5984 | 25.8959 | 26.7892 | 29.265 | 29.8402 | SSIM | 0.5246 | 0.562 | 0.5339 | 0.6021 | 0.7312 | 0.7831 |
| σ = 20 | PSNR | 22.0996 | 23.2623 | 25.2557 | 26.3092 | 28.5353 | 28.9227 | SSIM | 0.4058 | 0.449 | 0.4725 | 0.5579 | 0.7035 | 0.7343 |
| σ = 25 | PSNR | 20.1693 | 21.6197 | 24.6607 | 25.8326 | 27.6622 | 27.9387 | SSIM | 0.3205 | 0.3707 | 0.4226 | 0.5176 | 0.6634 | 0.6823 |
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