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This paper applies K-Means clustering for patch-based image inpainting: it learns a texture dictionary from intact grayscale regions, then uses it to predict missing pixels. The method is computationally efficient, validated on sparse random damage, and performance is measured as corruption increases.

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A milestone report for the Data Science Capstone project on introducing a predictive text model

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