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Fig.3.Input Image Fig.4.Noisy Image filtering denoising method for the random gaussian noise density 0.01. The PSNR of restored image is 31.6564 dB. Fig.6.Input Image Fig. 7.Noisy Image Fig.5.Denoised Image Fig.3 is the original image. Fig.4 is the noisy image. The random noise is added in the original image. Fig.5 shows thedenoised image with preserving more details for noise density 0.01. B.SYSGEN Results The Fig.8 shows the system generator model result of bilateral Fig. 8. Denoised Image Fig.8 shows the system generator results for the image "tire" with noise density 0.01. Fig.6is the original image. Fig. 7 is the noisy image. The random gaussian noise is added in the original image. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) 301

          Fig.3.Input Image
Fig.4.Noisy Image
filtering denoising method for the random gaussian noise
density 0.01. The PSNR of restored image is 31.6564 dB.
Fig.6.Input Image
Fig. 7.Noisy Image
Fig.5.Denoised Image
Fig.3 is the original image. Fig.4 is the noisy image. The
random noise is added in the original image. Fig.5 shows
thedenoised image with preserving more details for noise
density
0.01.
B.SYSGEN Results
The Fig.8 shows the system generator model result of bilateral
Fig. 8. Denoised Image
Fig.8 shows the system generator results for the image "tire"
with noise density 0.01. Fig.6is the original image. Fig. 7 is the
noisy image. The random gaussian noise is added in the
original image.
2015 International Conference on Green Computing and Internet of Things (ICGCIoT)
301

Fig.3.Input Image
Fig.4.Noisy Image
filtering denoising method for the random gaussian noise
density 0.01. The PSNR of restored image is 31.6564 dB.
Fig.6.Input Image
Fig. 7.Noisy Image
Fig.5.Denoised Image
Fig.3 is the original image. Fig.4 is the noisy image. The
random noise is added in the original image. Fig.5 shows
thedenoised image with preserving more details for noise
density
0.01.
B.SYSGEN Results
The Fig.8 shows the system generator model result of bilateral
Fig. 8. Denoised Image
Fig.8 shows the system generator results for the image "tire"
with noise density 0.01. Fig.6is the original image. Fig. 7 is the
noisy image. The random gaussian noise is added in the
original image.
2015 International Conference on Green Computing and Internet of Things (ICGCIoT)
301

Added by Janice W.

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