M
Min Li
Researcher at University of California, San Diego
Publications - 6
Citations - 191
Min Li is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Interpolation & Markov random field. The author has an hindex of 5, co-authored 6 publications receiving 177 citations. Previous affiliations of Min Li include University of San Diego.
Papers
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Journal ArticleDOI
Markov Random Field Model-Based Edge-Directed Image Interpolation
Min Li,Truong Q. Nguyen +1 more
TL;DR: This paper presents an edge-directed image interpolation algorithm that improves the subjective quality of the interpolated edges while maintaining a high PSNR level and a single-pass implementation is designed, which performs nearly as well as the iterative optimization.
Journal ArticleDOI
A De-Interlacing Algorithm Using Markov Random Field Model
Min Li,Truong Q. Nguyen +1 more
TL;DR: This paper proposes a motion-compensated de-interlacing algorithm using the Markov random field model, which is implemented by an iterative optimization process, which guarantees convergence, however, a global optimal solution is not guaranteed due to computational complexity concern.
Proceedings ArticleDOI
Markov Random Field Model-Based Edge-Directed Image Interpolation
Min Li,Truong Q. Nguyen +1 more
TL;DR: Simulation and comparison results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity.
Proceedings ArticleDOI
Optimal wavelet filter design in scalable video coding
Min Li,Truong Q. Nguyen +1 more
TL;DR: The design of a class of wavelet filters and its application in scalable video coding (SVC) is discussed in detail and the simulation that compares the performances of the designed filters and Daubechies (9,7) filters in SVC is illustrated.
Proceedings ArticleDOI
Discontinuity-Adaptive De-Interlacing Scheme Using Markov Random Field Model
Min Li,Truong Q. Nguyen +1 more
TL;DR: A de-interlacing algorithm to find the optimal deinterlaced results given accuracy-limited motion information is proposed and simulation results show that the MAP-MRF formulation is efficient and the high frequency noise is removed in a few iterations.