所谓超分辨率(SR)技术就是由低分辨率(LR)图像序列来重建高分辨率(HR)图像的技术，而基于压缩图像的SR技术正成为当前研究的热点。为了提高压缩图像的重建质量，在正则化理论的基础上，通过利用比特流中的信息，提出了一种新颖的空时自适应超分辨率重建算法，该算法先利用正则化代价函数控制时域数据和空域先验信息之间的平衡，使正则化参数在SR重建过程中得到自适应地调整，然后利用迭代梯度下降法进行超分辨率重建。仿真实验表明，该自适应算法比采用传统算法重建的图像的主、客观质量有一定的提高, 适合压缩图像的应用。

Super resolution (SR) technique is the task of estimating High resolution (HR) images from a sequence of Low resolution (LR) observations, which has been a great focus for compressed images. Based on the theory of regularization, a novel spatio temporally adaptive SR algorithm is developed and analyzed using the information from the compressed bitstream. A new form of regularized cost function to control the balance between temporal data and spatial prior information is proposed. An iterative gradient descent algorithm is utilized to reconstruct the HR image. The regularization parameter is simultaneously estimated at each iteration step in the reconstruction process of the HR image. Experimental results demonstrate that the proposed algorithm has an improvement in terms of both objective and subjective quality，and it is applicable for compressed images.