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Publication 13-CNA-014
Suppression of Missing Data Artifacts for Deblurring Images Corrupted by Random Valued Noise Nam-Yong Lee Abstract: For deblurring images corrupted by random valued noise, two-phase methods first select likely-to-be reliables (data that are not corrupted by random valued noise) and then deblur images only with selected data. The selective use of data in two-phase methods, however, often causes missing data artifacts. In this paper, to suppress these missing data artifacts, we propose a blurring model based reliable-selection technique to select sufficiently many reliables so that all of to-be-recovered pixel values can contribute to selected data, while excluding random value noised data accurately. We also propose a normalization technique to compensate for non-uniform rates in recovering pixel values. Simulation studies show that proposed techniques effectively suppress missing data artifacts and, as a result, improve the performance of two-phase methods. Get the paper in its entirety as |
