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Technical Program
Paper Detail
| Paper: | MA-P5.6 |
| Session: | Image Representation, Rendering, Display, and Assesment |
| Time: | Monday, October 9, 09:40 - 12:20 |
| Presentation: |
Poster
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| Title: |
DISCONTINUITY-ADAPTIVE DE-INTERLACING SCHEME USING MARKOV RANDOM FIELD MODEL |
| Authors: |
Min Li; University of California, San Diego | | | | | Truong Nguyen; University of California, San Diego | | |
| Abstract: |
In this paper, a deinterlacing algorithm to find the optimal deinterlaced results given accuracy-limited motion information is proposed.The deinterlacing process is formulated as a Maximum A Posterior (MAP) - Markov Random Field (MRF)problem. The MAP solution is the one that minimizes an energy function. The energy function imposes discontinuity adaptive smoothness constraint upon the deinterlaced frame. Simulation results show that the {\small MAP-MRF} formulation is efficient and the high frequency noise is removed in a few iterations. |
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