Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Image Interpolation using Mathematical Morphology
DIAL '06 Proceedings of the Second International Conference on Document Image Analysis for Libraries
Fast Anisotropic Smoothing of Multi-Valued Images using Curvature-Preserving PDE's
International Journal of Computer Vision
Example-based single document image super-resolution: a global MAP approach with outlier rejection
Multidimensional Systems and Signal Processing
An image interpolation scheme for repetitive structures
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
New edge-directed interpolation
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
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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.