Gaussian Blur with Additive Gaussian Noise Sample Clauses

Gaussian Blur with Additive Gaussian Noise. ‌ Next, we add 1% Gaussian noise to the Gaussian blurred image and deblur it with different boundary conditions. The noisy blurred image is shown Reflective BC Periodic BC Zero BC deblurred image zoom: table zoom: face Synthetic BC Anti-reflective BC Figure 2.9: Deblurring results on motion blurred “Xxxxxxx” with different boundary conditions blurred image zoom: table zoom: face Figure 2.10: Noisy Gaussian blurred “Xxxxxxx” image Table 2.3: PSNRs of deblurring results on noisy Gaussian blurred “Xxxxxxx” Blurred image Zero Periodic Reflective Anti-ref Synthetic PSNR 24.5383 23.8341 25.4795 26.9879 27.0608 26.3866 iteration - 2 5 23 18 29 in Figure 2.10 and the deblurring results are shown in Figure 2.11. The corresponding PSNRs are shown in Table 2.3, and the relative error plot against iteration is shown in Figure 2.12(a). In this case, anti-reflective boundary conditions give the best result, re- flective boundary conditions the second best, and synthetic boundary condi- tions a close third. One may suggest that in the process of obtaining synthetic boundary conditions from the noisy image, noise is taken as image feature and incorrect boundary conditions are obtained. However, we believe this is not true; we applied the synthetic boundary conditions, obtained from the noisy blurred image, to deblur the corresponding noise-free blurred image, and obtained the very good results shown in Figure 2.13, with a PSNR of 28.5262dB. This illustrates that good synthetic boundary conditions can still be obtained from noisy images. In fact, except for some pixels near the boundary, it is difficult to de- Reflective BC Periodic BC Zero BC deblurred image zoom: table zoom: face Synthetic BC Anti-reflective BC Figure 2.11: Deblurring results on noisy Gaussian blurred “Xxxxxxx” with different boundary conditions