基于自适应约束正则HL-MRF先验模型的MAP超分辨率重建

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。

MAP super-resolution reconstruction based on adaptive constraint regularization HL-MRF prior model

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 linear constraint for low frequency and a less punishment for high frequency. The model gained its optimal solution of parameters 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 performace in details, therefore it has ceratin advantage to suppress the high frequency noise and avoid excessively smoothing image details.