Selective data pruning-based compression using high-order edge-directed interpolation
IEEE Transactions on Image Processing
Nonlinear image upsampling method based on radial basis function interpolation
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Super-resolution texture synthesis using stochastic PAR/NL model
Journal of Visual Communication and Image Representation
Improved pre-processing algorithm in spatial scalability for scalable video coding
Proceedings of the 2013 Research in Adaptive and Convergent Systems
| Hi-index | 0.01 |
This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.