A Bayesian Super Resolution Algorithm Based on Synthetic Gradient Distribution

作     者：陈文 方向忠 

作者机构：Shanghai Key Laboratory of Digital Media Processing and Transmissions Institute of Image Communication and Information Processing Shanghai Jiaotong University 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2011年第28卷第3期

页      面：305-311页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 

基　　金：National Natural Science Foundations of China(No.60705012 No.60802025) 

主　　题：synthetic gradients Lorentzian distribution threshold edge preservation 

摘      要：A novel Bayesian super resolution (SR) algorithm based on the distribution of synthetic gradient is proposed. The synthetic gradient combines prior information in horizontal, vertical, and diagonal directions. Its distribution is modeled as a Lorentzian function and regarded as a new image model which can sufficiently regularize the ill-posed algorithm and preserve the edges in the reconstructed images. The graduated nonconvexity (GNC) optimization is employed to guarantee the convergence of the proposed Lorentzian SR (LSR) algorithm to the global minimum. The performance of LSR is compared with conventional algorithms, and experimental results demonstrate that the proposed algorithm obtains both subjective and objective gains.