基于重建的超分辨率(SR)方法中，图像求解是典型的高维病态问题，需借助有效的正则来稳定求解。在Nguyen等人的正则有参超分辨率盲恢复框架(RPSR)基础上，引入基于图像局部光滑特征的正则处理，提出自适应正则的有参超分辨率方法(ARPSR)，并从方便计算的角度，进一步提出了ARPSR的近似求解方法，即先将ARPSR问题，化为两个RPSR问题的带权组合，然后用RPSR框架估计图像模糊系统的自由参数和最优正则参数，用重排系统矩阵的方法构造预处理器，最后用预处理共轭梯度方法(PCG)求解超分辨率图像。算法分析和试验结果表明，ARPSR方法是对RPSR框架的进一步改进。

Image super-resolution restoration and enhancement (SR) based on reconstruction is a typically ill-posed and high-dimensional problem, which needs effective regularization to stable the solution. Lately a parametric and regularized blind SR( RPSR) was proposed by Nguyen et al, which has set up a frame work for the blind SR. Under the frame of RPSR, in this paper, an adaptive RPSR(ARPSR) based on image locale smoothing characteristics is put forward, and for the conveniences of computing, an approximate ARPSR is proposed also, by which at first the ARPSR problem is transformed into a weighted combination of two RPSR problems, then the optical blurring and regularization free parameters are estimated by the standard RPSR frame, and then by exploiting the structures of the reordered system matrices, a preconditioner is constructed for the preconditioned conjugate gradient method(PCG) by which the high-resolution image is solved at last. Computational analyses and experimental results with synthetic low-resolution sequences show the improvements of ARPSR to the RPSR frame.