Super-Resolution Reconstruction for Face Images Based on Particle Filters Method
基于粒子滤波的人脸图像超分辨率重建方法

Super-Resolution (SR) reconstruction is posed as a Bayesian estimation of the location and appearance parameters of a face model. Image registration and image fusion, the two steps for SR reconstruction, are combined into one unified probabilistic framework, in which the prior information about facial appearance and gray from the face model is incorporated into both of the steps. In addition, a particle filter based algorithm is proposed to achieve the estimation, i.e. SR reconstruction. The proposed approach avoids the inherent dilemma of the most traditional methods, in which it demands a high-resolution image to get an accurate and robust estimation of the motion field, while reconstructing a high-resolution image requires the accurate and robust estimation of motion field. Experiments performed on synthesized frontal face sequences show that the proposed approach gains superior performance both in registration and reconstruction.