A Bayesian Super Resolution Algorithm Based on Synthetic Gradient Distribution  

作　　者：陈文 方向忠 

机构地区：[1]Shanghai Key Laboratory of Digital Media Processing and Transmissions, Institute of Image Communication and Information Processing, Shanghai Jiaotong University

出　　处：《Journal of Donghua University(English Edition)》2011年第3期305-311,共7页东华大学学报（英文版）

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

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.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.

关 键 词：synthetic gradients Lorentzian distribution THRESHOLD edge preservation 

分 类 号：TN911.73[电子电信—通信与信息系统]