基于特征表征的单幅图像超分辨方法（英文）

Single Image Super-Resolution Based on the Feature Sign Method

基于稀疏表示的图像超分辨是近年信号处理中的研究热点,快速准确地找到图像的稀疏表示系数是该方法的关键。该文提出了一种基于特征表征的算法来求解图像块的稀疏表示系数。受压缩感知理论启发,使用联合训练的字典来进行图像超分辨。特征表征算法在每一次迭代中,通过确定稀疏系数的符号,将求解的非凸问题变为凸问题,有效提高所得稀疏系数的准确性和超分辨算法速度。仿真结果显示,与插值法和经典的稀疏表示法比较,特征表征法可以得到更好的主观视觉评价和客观量化评价。

Recently, the super-resolution methods based on sparse representation has became a research hotpot in signal processing. How to calculate the sparse coefficients fast and accurately is the key of sparse representation algorithm. In this paper, we propose a feature sign method to compute the sparse coefficients in the search step. Inspired by the compressed sensing theory, two dictionaries are jointly learnt to conduct super-resolution in this method. The feature sign algorithm changes the non-convex problem to a convex one by guessing the sign of the sparse coefficient at each iteration. It improves the accuracy of the obtained sparse coefficients and speeds the algorithm. Simulation results show that the proposed scheme outperforms the interpolation methods and classic sparse representation algorithms in both subjective inspects and quantitative evaluations.