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(1) An EC roughness model is presented to characterize the roughness by using the Gaussian filter.
Image de-noising techniques using the Gaussian filter has been widely used in many fields for its ability to efficiently restore degraded data.
Figure 4 shows the trajectory generated by considering the horizontal translational factor (blue) and the obtained smoothing (green) using the Gaussian filter with σ=40 and the adaptive version proposed in this work.
Therefore, the dehazed images obtained using the Gaussian filter have even higher Q e and Q g values than those obtained using the soft matting (in Fig. 26, Gaussian filter: Q e = 1.7311, Q g = 1.1073; soft matting: Q e = 1.1675, Q g = 0.8774).
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In the first version, we used the Gaussian filter considering σ=40.
The RCR method uses the Gaussian filter to smooth the enhanced depth map, the enhanced depth map is called the guided depth map after the Gaussian filter, then the guided depth map is used to iterate the original enhanced depth map and sharpen the tiny jagged edges.
For example, in [2], Eva and Aishy used the Gaussian filter for image de-noising where they studied the relation between Gaussian scale space and linear diffusion, and they derived an estimation of the Gaussian variance and window size; this technique is called AGSS.
Firstly, the "TVGF" model in [7] uses the Gaussian filtering to approximation.
Besides, in [27, 38], the authors used the Gaussian filtering processing to regularize the level set function.
Blur the background by using the Gaussian Blur filter (Filters -> Blur -> Gaussian Blur).
The values of the Kernel matrix (filter mask) (Gonzales et al. 2004; Gonzales and Woods 2002) used by the Gaussian filter, are calculated using the Eqs.
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