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According to the determined edge direction, the missing pixels are interpolated with weighting functions.
In this case, the missing pixels are interpolated by the weighted average of neighboring pixels.
If the edge types are clearly determined as or, the missing pixels are interpolated toward the direction.
In some methods [20 22], the edge directions of missing pixels are indirectly estimated in aid of the additional information from the horizontally and vertically prereconstructed images.
In [9], in order to enlarge the image, it is down-sampled by using the bilinear method and then, the missing pixels are estimated by using a combination of the other pixels, while the desired coefficients are optimized by using a least mean square technique.
Missing pixels are estimated using an interpolation process.
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Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR.
The measurements were corrected using the midway infrared equalization algorithm, and the missing pixels were solved with the regularized orthogonal matching pursuit algorithm.
Because the pixels with real values neighboring a missing pixel are exclusively located in a diagonal direction, the diagonal residual value is first calculated as {{begin{aligned} widehatlambda left i,jright)&=w_{NE}lambda left i-1,j+1right)+w_{SE}left i-1eft(i+w_{SE}lambda&quad+w_{SW}leftda left(i+1,j-1right)+w_{NW}lambda left(i-1,j-1right), end{aligned}}} (19).
The edge indicator functions in several directions are defined as measures of edge information and a missing pixel is determined as a weighted sum of its neighbors.
In order to obtain progressive images, missing pixels have to be reconstructed using linear filters according to the spatial correlations, the temporal correlations, and both the spatial and temporal correlations in interlaced video sequences.
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