Contaminated Images Recovery by Implementing Non-negative Matrix Factorisation
This work addresses image recovery from corruption for computer vision applications, but it is incremental as it builds on existing NMF methods without achieving full convergence.
The study examined the robustness of Non-negative Matrix Factorisation (NMF) algorithms, including HCNMF and L2,1-NMF, for recovering contaminated images on ORL and Extended YaleB datasets, finding that algorithms required different iteration counts and did not fully converge due to computational constraints.
Non-negative matrix factorisation (NMF) has been extensively applied to the problem of corrupted image data. Standard NMF approach minimises Euclidean distance between data matrix and factorised approximation. The traditional NMF technique is sensitive to outliers since it utilises the squared error of each data point, despite the fact that this method has proven effective. In this study, we theoretically examine the robustness of the traditional NMF, HCNMF, and L2,1-NMF algorithms and execute sets of experiments to demonstrate the robustness on ORL and Extended YaleB datasets. Our research indicates that each algorithm requires a different number of iterations to converge. Due to the computational cost of these approaches, our final models, such as the HCNMF and L2,1-NMF model, fail to converge within the iteration parameters of this work. Nonetheless, the experimental results illustrate, to some extent, the robustness of the aforementioned techniques.