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Many demosaicing algorithms attempt to preserve or enhance edge components in the image.
Hence, eliminating the background components in the image block can be useful for estimating the Bayer pattern configuration.
For each binary image, the level set segmentation was used to generate a 42-bin histogram for the connected components in the image.
The solution used for estimating H s came form the existence of the BGN components in the image spectrum I k, ω) (see Figure 4).
The product functions thus obtained are added up to form a single resultant product function, whose optical Fourier transformation gives the correlation peaks for the presence of R, G and B components in the image.
If we fit the coefficients of AC components in the image patch to a Laplacian model, the standard deviations of E u, v AJ B i can be directly calculated with the quality factor QF.
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Also, the proposed IntFLTs can preserve the high frequency components in the images.
Since the almost conspicuous regions are not from the periodic pattern but from the edge components in the images, the conspicuous regions disturb the detecting process.
However, it cannot preserve the high frequency components in the images as shown in Figure 4, whereas the proposed IntFLTs, especially the proposed 16 × 32 IntFLT, can preserve them.
Because SNRs in individual particles drop quickly with increasing spatial frequency image alignments are typically mainly driven by the lower-resolution components in the images (Henderson et al., 2011; Scheres and Chen, 2012).
The segmentation is applied for every blob or connected component in the image in isolation.
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