Abstract
Non-negative matrix factorization (NMF) and its improvement methods fail to image reconstruction while the image dataset is contaminated by salt and pepper noise. To address this issue, robust Manhattan NMF (RMahNMF) based matrix completion is proposed to restore the corrupted data. Experiments show that RMahNMF is more effective and robust in image reconstruction than other NMF methods.
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