In this paper, an super-resolution imaging reconstruction algorithm based on the parametric adaptive online dictionary learning (ODL) is proposed. Under the framework of the classical sparse representation algorithm, the ODL method is used to improve the accuracy of dictionary learning. Furthermore, the regularization parameters in the sparse reconstruction stage are flexibly adjusted using the parameter adaptive method, so that the regularization parameters can be adaptively determined based on the characteristics of each image block, overcoming the disadvantages of the singularity and incompatibility of the artificially set parameters. Results show that compared with the traditional algorithm, the proposed algorithm can reduce the dependence of test images on the training image set, overcome the local blur or distortion in the reconstruction process, and improve the quality of the reconstructed image.