International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
IC for motion-compensated de-interlacing, noise reduction, and picture-rate conversion
IEEE Transactions on Consumer Electronics
Adaptive scan rate up-conversion system based on human visual characteristics
IEEE Transactions on Consumer Electronics
Performance analysis of motion-compensated de-interlacing systems
IEEE Transactions on Image Processing
A De-Interlacing Algorithm Using Markov Random Field Model
IEEE Transactions on Image Processing
Robust methods for high-quality stills from interlaced video in the presence of dominant motion
IEEE Transactions on Circuits and Systems for Video Technology
Motion compensation assisted motion adaptive interlaced-to-progressive conversion
IEEE Transactions on Circuits and Systems for Video Technology
Hybrid de-interlacing algorithm based on motion vector reliability
IEEE Transactions on Circuits and Systems for Video Technology
Video de-interlacing by adaptive 4-field global/local motion compensated approach
IEEE Transactions on Circuits and Systems for Video Technology
An adaptive motion-compensated approach for video deinterlacing
Multimedia Tools and Applications
Iterative second-order derivative-based deinterlacing algorithm
Image Communication
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In this paper, we propose an MRF-based deinterlacing algorithm that combines the benefits of rule-based algorithms such as motion-adaptation, edge-directed interpolation, and motion compensation, with those of anMRF formulation. MRF-based interpolation and enhancement algorithms are typically formulated as an optimization over pixel intensities or colors, which can make them relatively slow. In comparison, our MRF-based deinterlacing algorithm uses interpolation functions as labels. We use seven interpolants (three spatial, three temporal, and one for motion compensation). The core dynamic programming algorithm is, therefore, sped up greatly over the direct use of intensity as labels. We also show how an exemplar-based learning algorithm can be used to refine the output of our MRF-based algorithm. The training set can be augmented with exemplars from static regions of the same video, as a form of "self-learning."