Markov Random Field Model-Based Edge-Directed Image Interpolation
This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. Consequently, the local edge directions are represented by length-16 vectors, which are denoted as weight vectors. The weight vectors are used to formulate geometric regularity constraint, which is imposed on the interpolated image through the Markov Random Field (MRF) model. Furthermore, the interpolation problem is formulated as a Maximum A Posterior (MAP)-MRF problem and, under the MAP-MRF framework, the desired interpolated image corresponds to the minimal energy state of a two-dimensional random held. Simulated Annealing method is used to search for the minimal energy state from a reasonable large state space. Simulation and comparison results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity.