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This method performs the super-resolution by treating this problem as a missing intensity interpolation problem.
Therefore, the method in[24] is suitable in confirming the effectiveness of the proposed inpainting algorithm, i.e., the missing intensity estimation algorithm.
In a first step, missing intensity values were imputed.
Data analysis was optimized by imputing missing intensity values and subsequent data normalisation.
Probes with missing intensity signals were discarded (21 098 probes out of 485 577).
These values were used to impute missing intensity values as described in the methods section.
Similar(47)
Therefore, the proposed method clips patches including missing areas and performs inpainting to estimate all missing intensities.
Furthermore, we have also proposed a method for reconstructing missing intensities based on a new classification scheme [28].
Specifically, the proposed method performs the sparse representation of the target patch to estimate the missing intensities.
As shown in the above procedures, we can estimate the missing intensities in Ω within the target patch f.
Generally, since the restoration of missing areas is an ill-posed problem, it is difficult to directly estimate the missing intensities.
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