摘要
针对Huber-MRF先验模型对图像高频噪声抑制能力较差,而Gauss-MRF先验模型对图像高频过度惩罚的问题,提出了一种改进的自适应约束正则HL-MRF先验模型。该模型将Huber边缘惩罚低频函数与Lorentzian边缘惩罚高频函数相结合,对低频进行线性约束的同时对高频实现平滑惩罚;并采用自适应约束方法确定正则化参数,从而得到最优的参数解。与基于Gauss-MRF先验模型和Huber-MRF先验模型的超分辨率算法相比,HL-MRF先验模型获得的超分辨率重建图像在峰值信噪比(PSNR)和细节方面都有一定程度的提高,在抑制高频噪声、避免图像细节被过度平滑方面具有一定的优势。
Aiming at the poor suppression ability for the high-frequency noise in Huber-MRF prior model and the excessive punishment for the high frequency information of image in Gauss-MRF prior model, an adaptive regularization HL- MRF model was proposed. The method combined low frequency function of Huber edge punishment with high frequency function of Lorentzian edge punishment to realize a h:aear constraint for low frequency and a less punishment for high frequency. The model gained its optimal solution of parmneters by using adaptive constraint method to determine regularization parameter. Compared with super-resolution reconstruction methods based on Gauss-MRF prior model and Huber-MRF prior model, the method based on HL-MRF prior model obtains higer Peak Signal-to-Noise Ratio (PSNR) and better pefformace in details, therefore it has ceratin advantage to suppress the high frequency noise and avoid excessively smoothing image details.
出处
《计算机应用》
CSCD
北大核心
2015年第2期506-509,共4页
journal of Computer Applications
基金
陕西省自然科学基金资助项目(2013JM8025)
关键词
超分辨率重建
马尔可夫随机场先验模型
自适应正则化
边缘惩罚函数
super-resolution reconstruction
Markov Random Field (MRF) prior model
adaptive regularization
edge penalty function