Deep learning based super-resolution methods have received much attention, especially unsupervised super-resolution due to the difficulty of collecting images pairs (low-resolution and high-resolution images from the same scenario) in many fields, such as optics. Optical imaging is typical technique in advance optical measurement equipment and optical super-resolution imaging has received much attention. In this paper, a novel model, deep image prior with design surface model (DIP-DSM), based on deep image prior to improve the resolution of optical imaging is presented. It makes use of single image instead of using random input in which the design surface model is regarded as prior information. To validate the model, a series of experiments are conducted, and the results show the superiority of the proposed model as compared with deep image prior. Furthermore, the performance of different neural networks are explored and it is find that the U-Net achieve best reconstruction quality and reach to PSNR, 32.937.