Abstract
This paper introduces a Bayesian restoration method for low-resolution images combined with a geometry-driven smoothness prior and a new global multimodal prior. The multimodal prior is proposed for images that normally just have a few dominant colours. In spite of this, most images contain much more colours due to noise and edge pixels that are part of two or more connected smooth regions. The Maximum A Posteriori estimator is worked out to solve the problem. Experimental results confirm the effectiveness of the proposed global multimodal prior for images with a strong multimodal colour distribution such as cartoons. We also show the visual superiority of our reconstruction scheme to other traditional interpolation and reconstruction methods: noise and compression artifacts are removed very well and our method produces less blur and other annoying artifacts.
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Luong, H., Goossens, B., Philips, W. (2007). Image Upscaling Using Global Multimodal Priors. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_43
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DOI: https://doi.org/10.1007/978-3-540-74607-2_43
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