Figure 1.
Ground-truth images.
Figure 1.
Ground-truth images.
Figure 3.
PSNR result with the change in and p. PSNR values are color-coded from red (lowest: 14.01 dB) to green (highest: 42.68 dB), with intermediate values transitioning through yellow.
Figure 3.
PSNR result with the change in and p. PSNR values are color-coded from red (lowest: 14.01 dB) to green (highest: 42.68 dB), with intermediate values transitioning through yellow.
Figure 4.
Examples of building image with mark recovery.
Figure 4.
Examples of building image with mark recovery.
Figure 5.
Examples of building image with random recovery.
Figure 5.
Examples of building image with random recovery.
Figure 6.
Examples of building image with scratch recovery.
Figure 6.
Examples of building image with scratch recovery.
Figure 7.
Examples of building image with watermark recovery.
Figure 7.
Examples of building image with watermark recovery.
Figure 8.
Examples of cat image with mark recovery.
Figure 8.
Examples of cat image with mark recovery.
Figure 9.
Examples of cat image with random recovery.
Figure 9.
Examples of cat image with random recovery.
Figure 10.
Examples of cat image with scratch recovery.
Figure 10.
Examples of cat image with scratch recovery.
Figure 11.
Examples of cat image with watermark recovery.
Figure 11.
Examples of cat image with watermark recovery.
Figure 12.
Examples of face image with mark recovery.
Figure 12.
Examples of face image with mark recovery.
Figure 13.
Examples of face image with random recovery.
Figure 13.
Examples of face image with random recovery.
Figure 14.
Examples of face image with scratch recovery.
Figure 14.
Examples of face image with scratch recovery.
Figure 15.
Examples of face image with watermark recovery.
Figure 15.
Examples of face image with watermark recovery.
Figure 16.
Examples of forest image with mark recovery.
Figure 16.
Examples of forest image with mark recovery.
Figure 17.
Examples of forest image with random recovery.
Figure 17.
Examples of forest image with random recovery.
Figure 18.
Examples of forest image with scratch recovery.
Figure 18.
Examples of forest image with scratch recovery.
Figure 19.
Examples of forest image with watermark recovery.
Figure 19.
Examples of forest image with watermark recovery.
Figure 20.
Examples of fox image with mark recovery.
Figure 20.
Examples of fox image with mark recovery.
Figure 21.
Examples of fox image with random recovery.
Figure 21.
Examples of fox image with random recovery.
Figure 22.
Examples of fox image with scratch recovery.
Figure 22.
Examples of fox image with scratch recovery.
Figure 23.
Examples of fox image with watermark recovery.
Figure 23.
Examples of fox image with watermark recovery.
Figure 24.
Examples of penguin image with mark recovery.
Figure 24.
Examples of penguin image with mark recovery.
Figure 25.
Examples of penguin image with random recovery.
Figure 25.
Examples of penguin image with random recovery.
Figure 26.
Examples of penguin image with scratch recovery.
Figure 26.
Examples of penguin image with scratch recovery.
Figure 27.
Examples of penguin image with watermark recovery.
Figure 27.
Examples of penguin image with watermark recovery.
Figure 28.
Examples of rabbit image with mark recovery.
Figure 28.
Examples of rabbit image with mark recovery.
Figure 29.
Examples of rabbit image with random recovery.
Figure 29.
Examples of rabbit image with random recovery.
Figure 30.
Examples of rabbit image with scratch recovery.
Figure 30.
Examples of rabbit image with scratch recovery.
Figure 31.
Examples of rabbit image with watermark recovery.
Figure 31.
Examples of rabbit image with watermark recovery.
Figure 32.
Examples of image inpainting and denoising with mask mark in exponential noise and Gamma noise.
Figure 32.
Examples of image inpainting and denoising with mask mark in exponential noise and Gamma noise.
Figure 33.
Examples of image inpainting and denoising with mask mark in Poisson noise and Rayleigh noise.
Figure 33.
Examples of image inpainting and denoising with mask mark in Poisson noise and Rayleigh noise.
Figure 34.
Examples of image inpainting and denoising with mask mark in salt-and-pepper noise and uniform noise.
Figure 34.
Examples of image inpainting and denoising with mask mark in salt-and-pepper noise and uniform noise.
Figure 35.
Examples of image inpainting and denoising with mask random in exponential noise and Gamma noise.
Figure 35.
Examples of image inpainting and denoising with mask random in exponential noise and Gamma noise.
Figure 36.
Examples of image inpainting and denoising with mask random in Poisson noise and Rayleigh noise.
Figure 36.
Examples of image inpainting and denoising with mask random in Poisson noise and Rayleigh noise.
Figure 37.
Examples of image inpainting and denoising with mask random in salt-and-pepper noise and uniform noise.
Figure 37.
Examples of image inpainting and denoising with mask random in salt-and-pepper noise and uniform noise.
Figure 38.
Examples of image inpainting and denoising with mask scratch in exponential noise and Gamma noise.
Figure 38.
Examples of image inpainting and denoising with mask scratch in exponential noise and Gamma noise.
Figure 39.
Examples of image inpainting and denoising with mask scratch in Poisson noise and Rayleigh noise.
Figure 39.
Examples of image inpainting and denoising with mask scratch in Poisson noise and Rayleigh noise.
Figure 40.
Examples of image inpainting and denoising with mask scratch in salt-and-pepper noise and uniform noise.
Figure 40.
Examples of image inpainting and denoising with mask scratch in salt-and-pepper noise and uniform noise.
Figure 41.
Examples of image inpainting and denoising with mask watermark in exponential noise and Gamma noise.
Figure 41.
Examples of image inpainting and denoising with mask watermark in exponential noise and Gamma noise.
Figure 42.
Examples of image inpainting and denoising with mask watermark in Poisson noise and Rayleigh noise.
Figure 42.
Examples of image inpainting and denoising with mask watermark in Poisson noise and Rayleigh noise.
Figure 43.
Examples of image inpainting and denoising with mask watermark in salt-and-pepper noise and uniform noise.
Figure 43.
Examples of image inpainting and denoising with mask watermark in salt-and-pepper noise and uniform noise.
Figure 44.
Examples of image inpainting and denoising with mask mark in Gaussian noise.
Figure 44.
Examples of image inpainting and denoising with mask mark in Gaussian noise.
Figure 45.
Examples of image inpainting and denoising with mask random in Gaussian noise.
Figure 45.
Examples of image inpainting and denoising with mask random in Gaussian noise.
Figure 46.
Examples of image inpainting and denoising with mask scratch in Gaussian noise.
Figure 46.
Examples of image inpainting and denoising with mask scratch in Gaussian noise.
Figure 47.
Examples of image inpainting and denoising with mask watermark in Gaussian noise.
Figure 47.
Examples of image inpainting and denoising with mask watermark in Gaussian noise.
Table 1.
Window size n and calculation time with a change in .
Table 1.
Window size n and calculation time with a change in .
| | 0.1 | 0.15 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 |
n | 1 | 67 | 42 | 84 | 81 | 73 | 63 | 54 | 46 | 40 | 34 |
Time (s) | 0.19 | 13.78 | 19.08 | 4.17 | 20.06 | 16.33 | 12.20 | 9.02 | 6.58 | 1.01 | 3.65 |
| 1.0 | 1.1 | 1.2 | 1.3 | 1.4 | 1.5 | 1.6 | 1.7 | 1.8 | 1.9 | |
n | 30 | 26 | 23 | 20 | 17 | 15 | 13 | 11 | 9 | 7 | 1 |
Time (s) | 2.87 | 2.18 | 1.73 | 0.31 | 0.99 | 0.79 | 0.62 | 0.47 | 0.35 | 10.25 | 0.19 |
Table 2.
Image inpainting comparison results in terms of PSNR and SSIM.
Table 2.
Image inpainting comparison results in terms of PSNR and SSIM.
| | TV | TGV | ftTV | AMSI | GLCIC | MEDFE | AOT-GAN | Ours |
---|
Image | Mask | | | | | | | | |
Building | Mark | 24.59 | 24.38 | 24.66 | 24.66 | 23.43 | 18.20 | 23.73 | 24.69 |
0.9409 | 0.9417 | 0.9424 | 0.9424 | 0.9344 | 0.6249 | 0.9358 | 0.9431 |
Random | 20.38 | 20.28 | 20.47 | 20.47 | 18.93 | 18.10 | 16.71 | 20.55 |
0.8359 | 0.8434 | 0.8403 | 0.8403 | 0.7944 | 0.6124 | 0.6821 | 0.8422 |
Scratch | 31.57 | 31.41 | 31.63 | 31.63 | 30.60 | 18.32 | 29.95 | 31.71 |
0.9884 | 0.9888 | 0.9887 | 0.9887 | 0.9864 | 0.6365 | 0.9806 | 0.9889 |
Watermark | 25.82 | 25.64 | 25.87 | 25.87 | 24.47 | 18.27 | 24.81 | 25.95 |
0.9546 | 0.9561 | 0.9556 | 0.9556 | 0.9449 | 0.6315 | 0.9381 | 0.9565 |
Cat | Mark | 41.20 | 42.04 | 41.85 | 41.85 | 39.71 | 32.37 | 34.51 | 42.73 |
0.9925 | 0.9935 | 0.9940 | 0.9940 | 0.9907 | 0.9460 | 0.9662 | 0.9948 |
Random | 39.42 | 38.63 | 40.24 | 40.23 | 33.17 | 35.94 | 21.30 | 40.62 |
0.9864 | 0.9839 | 0.9894 | 0.9894 | 0.9671 | 0.9753 | 0.6288 | 0.9900 |
Scratch | 49.18 | 49.81 | 50.06 | 50.06 | 47.89 | 37.03 | 39.43 | 50.41 |
0.9985 | 0.9987 | 0.9988 | 0.9988 | 0.9981 | 0.9772 | 0.9851 | 0.9989 |
Watermark | 43.98 | 44.89 | 44.82 | 44.82 | 41.19 | 36.82 | 35.47 | 45.22 |
0.9955 | 0.9962 | 0.9965 | 0.9965 | 0.9936 | 0.9767 | 0.9608 | 0.9967 |
Face | Mark | 39.22 | 39.77 | 39.96 | 39.96 | 38.43 | 31.54 | 34.75 | 40.21 |
0.9882 | 0.9895 | 0.9901 | 0.9901 | 0.9850 | 0.9239 | 0.9649 | 0.9905 |
Random | 36.67 | 36.05 | 38.13 | 38.13 | 30.81 | 33.46 | 14.51 | 38.88 |
0.9750 | 0.9758 | 0.9826 | 0.9826 | 0.9500 | 0.9419 | 0.4029 | 0.9844 |
Scratch | 47.56 | 48.27 | 48.80 | 48.80 | 46.82 | 34.53 | 39.20 | 49.55 |
0.9982 | 0.9985 | 0.9987 | 0.9987 | 0.9979 | 0.9552 | 0.9810 | 0.9988 |
Watermark | 41.01 | 41.85 | 42.16 | 42.17 | 39.91 | 34.31 | 33.45 | 42.70 |
0.9914 | 0.9927 | 0.9934 | 0.9934 | 0.9877 | 0.9526 | 0.9374 | 0.9940 |
Forest | Mark | 30.52 | 30.42 | 30.51 | 30.51 | 29.44 | 23.35 | 27.80 | 30.58 |
0.9488 | 0.9495 | 0.9496 | 0.9496 | 0.9434 | 0.6290 | 0.9155 | 0.9489 |
Random | 25.92 | 25.70 | 25.92 | 25.92 | 24.62 | 23.74 | 18.71 | 25.94 |
0.8370 | 0.8348 | 0.8382 | 0.8381 | 0.8000 | 0.6405 | 0.5602 | 0.8361 |
Scratch | 37.59 | 37.43 | 37.58 | 37.58 | 36.57 | 23.89 | 32.94 | 37.63 |
0.9899 | 0.9897 | 0.9898 | 0.9898 | 0.9880 | 0.6553 | 0.9699 | 0.9898 |
Watermark | 31.33 | 31.27 | 31.32 | 31.32 | 30.23 | 23.86 | 26.93 | 31.35 |
0.9559 | 0.9564 | 0.9563 | 0.9563 | 0.9486 | 0.6520 | 0.8901 | 0.9560 |
Fox | Mark | 41.07 | 41.26 | 41.24 | 41.24 | 39.54 | 31.16 | 35.10 | 41.34 |
0.9893 | 0.9898 | 0.9901 | 0.9901 | 0.9876 | 0.9107 | 0.9653 | 0.9902 |
Random | 36.90 | 34.10 | 37.23 | 37.22 | 34.71 | 34.21 | 16.75 | 36.96 |
0.9698 | 0.9610 | 0.9730 | 0.9730 | 0.9581 | 0.9389 | 0.5147 | 0.9731 |
Scratch | 48.04 | 48.05 | 48.51 | 48.51 | 47.00 | 34.62 | 35.07 | 48.63 |
0.9976 | 0.9977 | 0.9979 | 0.9979 | 0.9974 | 0.9416 | 0.9738 | 0.9979 |
Watermark | 41.61 | 42.01 | 41.98 | 41.98 | 40.02 | 34.56 | 32.69 | 42.19 |
0.9913 | 0.9919 | 0.9924 | 0.9924 | 0.9901 | 0.9410 | 0.9437 | 0.9925 |
Penguin | Mark | 39.08 | 40.12 | 39.48 | 39.48 | 37.94 | 30.10 | 33.38 | 40.45 |
0.9898 | 0.9912 | 0.9925 | 0.9925 | 0.9895 | 0.9208 | 0.9424 | 0.9935 |
Random | 37.18 | 34.80 | 37.48 | 37.48 | 34.37 | 33.22 | 19.25 | 37.60 |
0.9808 | 0.9708 | 0.9882 | 0.9882 | 0.9676 | 0.9633 | 0.4581 | 0.9897 |
Scratch | 46.34 | 46.43 | 46.61 | 46.61 | 45.71 | 34.33 | 38.20 | 46.93 |
0.9985 | 0.9986 | 0.9990 | 0.9990 | 0.9982 | 0.9733 | 0.9774 | 0.9991 |
Watermark | 42.13 | 42.76 | 42.42 | 42.42 | 40.12 | 34.12 | 31.85 | 43.12 |
0.9933 | 0.9941 | 0.9955 | 0.9955 | 0.9920 | 0.9708 | 0.9073 | 0.9962 |
Rabbit | Mark | 37.88 | 38.31 | 38.35 | 38.35 | 37.03 | 29.56 | 31.26 | 38.38 |
0.9951 | 0.9956 | 0.9957 | 0.9957 | 0.9948 | 0.9634 | 0.9662 | 0.9958 |
Random | 33.10 | 32.92 | 33.74 | 33.74 | 30.73 | 30.49 | 20.03 | 33.82 |
0.9849 | 0.9845 | 0.9873 | 0.9873 | 0.9761 | 0.9703 | 0.6611 | 0.9882 |
Scratch | 43.00 | 43.59 | 43.53 | 43.53 | 42.38 | 30.62 | 36.07 | 43.79 |
0.9986 | 0.9988 | 0.9988 | 0.9988 | 0.9985 | 0.9701 | 0.9885 | 0.9989 |
Watermark | 38.25 | 38.84 | 38.83 | 38.83 | 37.35 | 30.58 | 29.59 | 39.01 |
0.9955 | 0.9961 | 0.9962 | 0.9962 | 0.9951 | 0.9701 | 0.9461 | 0.9964 |
Table 3.
Image inpainting comparison results in terms of time.
Table 3.
Image inpainting comparison results in terms of time.
Time (s) | TV | TGV | ftTV | AMSI | GLCIC | MEDFE | AOT-GAN | Our |
---|
Mark | 7.868 | 203.646 | 6.865 | 35.524 | 0.549 | 0.122 | 1.101 | 44.721 |
Random | 5.722 | 171.770 | 4.785 | 31.137 | 15.692 | 0.061 | 1.099 | 15.175 |
Scratch | 4.395 | 138.177 | 3.948 | 34.716 | 0.611 | 0.061 | 1.098 | 12.106 |
Watermark | 5.556 | 166.638 | 4.710 | 35.190 | 0.795 | 0.065 | 1.096 | 17.969 |
Table 4.
Inpainting results for images with denoising in additive noise.
Table 4.
Inpainting results for images with denoising in additive noise.
Exponential Noise | Mark | Random | Scratch | Watermark |
---|
Building | 23.5219 0.9194 | 20.1381 0.8180 | 27.4947 0.9631 | 24.2992 0.9260 |
Cat | 31.3146 0.9546 | 30.5670 0.8946 | 31.4633 0.9561 | 31.2976 0.9601 |
Face | 30.8266 0.9121 | 30.5651 0.8991 | 31.1467 0.9190 | 30.9665 0.9139 |
Forest | 26.6060 0.8831 | 24.4644 0.7803 | 24.7045 0.9017 | 24.4132 0.8687 |
Fox | 30.3924 0.9374 | 30.7804 0.9306 | 30.8616 0.9489 | 30.5092 0.9463 |
Penguin | 30.5294 0.9402 | 30.7373 0.9271 | 30.7464 0.9524 | 30.8528 0.9482 |
Rabbit | 30.0392 0.9835 | 29.2628 0.9733 | 30.4775 0.9856 | 30.2554 0.9831 |
Gamma Noise
| Mark | Random | Scratch | Watermark |
Building | 23.5345 0.9191 | 20.1374 0.8177 | 27.4883 0.9629 | 24.3454 0.9278 |
Cat | 31.0909 0.9550 | 30.8529 0.9153 | 31.5462 0.9569 | 31.2054 0.9599 |
Face | 30.8984 0.9125 | 30.5897 0.8991 | 31.2104 0.9195 | 30.9707 0.9141 |
Forest | 26.7144 0.8839 | 24.4632 0.7803 | 24.8728 0.9033 | 25.8153 0.8861 |
Fox | 30.3399 0.9371 | 30.6962 0.9318 | 31.0956 0.9463 | 30.4271 0.9445 |
Penguin | 30.3736 0.9402 | 30.0680 0.9407 | 30.5794 0.9512 | 30.8383 0.9485 |
Rabbit | 30.0301 0.9834 | 29.2411 0.9735 | 30.4779 0.9855 | 30.2499 0.9831 |
Poisson noise | Mark | Random | Scratch | Watermark |
Building | 23.0979 0.8803 | 20.0634 0.7957 | 26.3553 0.9218 | 23.9323 0.8941 |
Cat | 32.3407 0.8950 | 30.0989 0.8327 | 32.6814 0.8973 | 33.1093 0.9077 |
Face | 34.1826 0.9593 | 33.1467 0.9480 | 35.2935 0.9677 | 34.4262 0.9620 |
Forest | 26.0793 0.7895 | 24.3409 0.7113 | 27.1964 0.8167 | 26.2036 0.7885 |
Fox | 32.6150 0.9036 | 31.3355 0.8669 | 32.3688 0.8871 | 31.6808 0.8722 |
Penguin | 31.4094 0.8124 | 31.2699 0.8321 | 32.1187 0.8392 | 32.0490 0.8556 |
Rabbit | 30.6436 0.9665 | 29.0811 0.9536 | 30.9963 0.9681 | 30.6285 0.9663 |
Rayleigh Noise | Mark | Random | Scratch | Watermark |
Building | 22.0915 0.9075 | 19.2451 0.7863 | 23.8002 0.9451 | 22.0345 0.9029 |
Cat | 26.1147 0.9420 | 26.2009 0.8374 | 25.9137 0.9446 | 25.7753 0.9302 |
Face | 25.9880 0.8552 | 26.2727 0.8352 | 25.8990 0.8602 | 26.0377 0.8520 |
Forest | 23.5722 0.8684 | 20.9930 0.7091 | 20.3927 0.8348 | 20.2043 0.7962 |
Fox | 25.3740 0.9284 | 26.7237 0.9003 | 25.5398 0.9386 | 25.3137 0.9317 |
Penguin | 25.7521 0.9295 | 26.8164 0.8680 | 25.6746 0.9465 | 26.0382 0.9304 |
Rabbit | 25.6232 0.9793 | 25.6816 0.9656 | 25.5858 0.9772 | 25.8484 0.9772 |
Salt-and-Pepper Noise | Mark | Random | Scratch | Watermark |
Building | 23.7426 0.9245 | 20.2712 0.8300 | 27.9141 0.9690 | 24.7012 0.9385 |
Cat | 35.1192 0.9645 | 33.1050 0.9574 | 35.9147 0.9675 | 35.4334 0.9653 |
Face | 34.2389 0.9563 | 33.5288 0.9552 | 35.4364 0.9650 | 34.4533 0.9589 |
Forest | 27.4748 0.8913 | 25.1746 0.8000 | 29.4569 0.9343 | 28.0936 0.9091 |
Fox | 33.7617 0.9350 | 33.3341 0.9375 | 34.6032 0.9487 | 34.1221 0.9484 |
Penguin | 33.8500 0.9537 | 33.1736 0.9554 | 34.3207 0.9584 | 33.9043 0.9584 |
Rabbit | 32.7510 0.9827 | 31.0736 0.9756 | 33.7107 0.9858 | 32.9432 0.9833 |
Uniform Noise | Mark | Random | Scratch | Watermark |
Building | 24.1427 0.9232 | 20.3883 0.8276 | 29.3024 0.9678 | 25.2204 0.9366 |
Cat | 36.2064 0.9609 | 33.7892 0.9299 | 36.7609 0.9615 | 36.9413 0.9657 |
Face | 35.2313 0.9574 | 34.4223 0.9449 | 36.8067 0.9655 | 35.7369 0.9597 |
Forest | 28.5367 0.8953 | 25.4301 0.7935 | 31.4508 0.9361 | 29.1955 0.9059 |
Fox | 34.3727 0.9396 | 34.1130 0.9350 | 35.6981 0.9513 | 35.3842 0.9494 |
Penguin | 34.9857 0.9465 | 33.8864 0.9462 | 35.8017 0.9582 | 34.9193 0.9567 |
Rabbit | 33.7146 0.9855 | 31.5987 0.9765 | 34.5199 0.9875 | 33.6418 0.9851 |
Table 5.
Norm and comparison in inpainting and denoising with standard deviation .
Table 5.
Norm and comparison in inpainting and denoising with standard deviation .
Image | | Mark | Random | Scratch | Watermark |
---|
Building | | 23.5013 0.9003 | 20.1948 0.8115 | 27.3354 0.9430 | 24.4183 0.9141 |
| 19.0140 0.7072 | 17.9458 0.6146 | 20.6456 0.8922 | 19.2468 0.7158 |
Cat | | 34.1832 0.9287 | 32.0829 0.8827 | 34.7117 0.9316 | 34.9303 0.9386 |
| 33.2248 0.9413 | 26.2452 0.8865 | 33.7216 0.9338 | 31.1999 0.9358 |
Face | | 34.0763 0.9382 | 33.0326 0.9189 | 35.1232 0.9451 | 34.2459 0.9382 |
| 33.8527 0.9334 | 32.7048 0.9077 | 33.3967 0.9112 | 33.1220 0.9095 |
Forest | | 27.5368 0.8575 | 25.0017 0.7635 | 29.4249 0.8916 | 27.8671 0.8614 |
| 22.8940 0.5629 | 23.1994 0.5984 | 23.4636 0.6089 | 23.8516 0.6505 |
Fox | | 33.7629 0.9287 | 32.9811 0.9125 | 34.3072 0.9302 | 33.8023 0.9237 |
| 34.1767 0.9322 | 33.0466 0.9171 | 32.9092 0.8976 | 33.6286 0.9184 |
Penguin | | 33.5357 0.9003 | 32.8536 0.9074 | 34.6136 0.9187 | 34.0214 0.9232 |
| 33.5320 0.9058 | 32.7783 0.8876 | 33.6433 0.8640 | 33.7135 0.8840 |
Rabbit | | 32.1686 0.9775 | 30.3236 0.9666 | 32.6918 0.9793 | 32.1175 0.9771 |
| 31.3957 0.9751 | 30.1936 0.9671 | 32.1703 0.9755 | 31.9561 0.9766 |
Table 6.
Norm and comparison in inpainting and denoising with standard deviation .
Table 6.
Norm and comparison in inpainting and denoising with standard deviation .
Image | | Mark | Random | Scratch | Watermark |
---|
Building | | 18.7108 0.6828 | 17.7757 0.6074 | 19.4323 0.7166 | 18.9695 0.6944 |
| 18.2491 0.6327 | 17.3849 0.5510 | 18.9258 0.6748 | 18.4319 0.6406 |
Cat | | 28.3353 0.8140 | 25.9881 0.7903 | 28.0589 0.7944 | 29.1774 0.8496 |
| 25.3165 0.6375 | 25.2741 0.6480 | 24.2393 0.5773 | 26.1413 0.6769 |
Face | | 26.6930 0.7640 | 25.0878 0.6956 | 26.6180 0.7679 | 25.7667 0.7324 |
| 24.2351 0.6294 | 22.0929 0.5284 | 21.4625 0.4956 | 21.5980 0.5028 |
Forest | | 22.5199 0.5496 | 22.2628 0.5150 | 22.6790 0.5607 | 22.5657 0.5521 |
| 22.5861 0.5406 | 22.3767 0.5265 | 22.8957 0.5670 | 22.9009 0.5713 |
Fox | | 27.6064 0.8030 | 27.3667 0.7853 | 27.0020 0.7763 | 26.6973 0.7511 |
| 24.7721 0.6116 | 25.3076 0.6448 | 21.3783 0.4194 | 23.2889 0.5236 |
Penguin | | 27.2103 0.7281 | 26.1111 0.6589 | 27.9634 0.7501 | 27.0132 0.6988 |
| 24.5030 0.4801 | 23.5745 0.4362 | 21.9900 0.3493 | 23.3234 0.4132 |
Rabbit | | 26.0637 0.9118 | 25.2335 0.9048 | 25.9572 0.9084 | 26.1536 0.9168 |
| 24.4858 0.8689 | 22.7802 0.8154 | 20.7357 0.7262 | 22.6686 0.8075 |