Adaptive Demosaicking using Multiple Neural Networks

Yangjing Long, Yizhen Huang
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
more » ... fferent chosen directions. Thus networks are specialized in learning and depicting non-linear spatial inter-pixel correlations at respective gradients and directions. Its performance surpasses Go's neural-network method greatly. Compared with 2 recent state-of-the-art methods, our method provides an excellent trade-off between computational expense and PSNR, and well preserves image edge information. As an extension, we compare the performance of these algorithms with and without Lukac's postprocessing.
doi:10.1109/mlsp.2006.275574 fatcat:m6fqvfqeundrpiacvzg5i7dxli