Demosaicing Based on Directional Difference Regression and Efficient Regression Priors | IEEE Journals & Magazine | IEEE Xplore

Demosaicing Based on Directional Difference Regression and Efficient Regression Priors


Abstract:

Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image. On the one hand, numerous interpolation methods focus...Show More

Abstract:

Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image. On the one hand, numerous interpolation methods focusing on spatial-spectral correlations have been proved very efficient, whereas they yield a poor image quality and strong visible artifacts. On the other hand, optimization strategies, such as learned simultaneous sparse coding and sparsity and adaptive principal component analysis-based algorithms, were shown to greatly improve image quality compared with that delivered by interpolation methods, but unfortunately are computationally heavy. In this paper, we propose efficient regression priors as a novel, fast post-processing algorithm that learns the regression priors offline from training data. We also propose an independent efficient demosaicing algorithm based on directional difference regression, and introduce its enhanced version based on fused regression. We achieve an image quality comparable to that of the state-of-the-art methods for three benchmarks, while being order(s) of magnitude faster.
Published in: IEEE Transactions on Image Processing ( Volume: 25, Issue: 8, August 2016)
Page(s): 3862 - 3874
Date of Publication: 01 June 2016

ISSN Information:

PubMed ID: 27254866

Funding Agency:


References

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