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Adaptive Demosaicking using Multiple Neural Networks
2006
Machine Learning for Signal Processing
Demosaicking is one of the important tasks in the imageprocessing pipeline in digital cameras using a single electronic sensor overlaid with a Color Filter Array. We quantitatively shows that, demosaicking algorithms perform better in low-gradient flat areas than in high-gradient steep areas. Based on this, an adaptive scheme is proposed that uses more complex neural networks to tackle steep areas in larger sizes of neighborhoods. And interpolation is edge-directed with different networks for
doi:10.1109/mlsp.2006.275574
fatcat:m6fqvfqeundrpiacvzg5i7dxli