基于MAP框架的金字塔人脸超分辨率算法

Pyramid Face Supper-resolution Algorithm Based on MAP Frame

提出一种基于学习的金字塔人脸超分辨率算法,利用金字塔学习人脸图像梯度的空间分布特性,建立标准人脸训练库作为学习模型,采用塔状父结构从训练库搜索匹配特征信息相似度最高的小块,预测出最优的拉普拉斯金字塔先验模型,利用贝叶斯MAP框架求出高分辨率人脸图像。实验结果表明,与其他人脸超分辨率算法相比,在将人脸图像分辨率提高4×4倍的情况下,该算法生成的高分辨率人脸图像的平均峰值信噪比提高1.19 dB^2.4 dB,可以更好地消除噪声,具有较好的视觉效果。

A new learning-based super-resolution algorithm is presented.Pyramid is used to extract the facial gradient distribution features,the standard face training database is established for the study model,these features are combined with pyramid-like parent structure to predict the best prior.And through the Bayesian Maximum A Posterior（MAP） frame,the high resolution face image is captured.Experimental results show that the proposed algorithm synthesizes high-resolution faces and eliminates the noise with better visual effect,and the average of peak signal-to-noise ratios is improved about 1.19 dB to 2.4 dB compared with some existing face super-resolution algorithms.