The dual resolution camera combines large field of view and high resolution. The coaxial optical fixed structure avoids many problems caused by the moving parts of the zoom lens, and has potential application value in deep space detection target tracking and smart terminals such as mobile phones. The existing dual resolution image zoom algorithm based on deep learning has slow speed, no increase in information, poor adaptability of image network structure and forgery of image information restoration. To join a solution in normal image algorithm based on depth information was tentatively proposed in this paper. The feasibility of using image focus sharpness as depth information into the dual resolution zoom algorithm was demonstrated, the accuracy and effect of focus depth information detection was explored by Laplace image evaluation, and the dual resolution zoom algorithm of deep learning and traditional methods based on depth information was tested. The new excellent zoom algorithm used the space during normal focus of the camera which did not affect the imaging speed of normal images. The algorithm complexity is reduced by 60%, the computational memory overhead is reduced by 35%. The spatial relationship super-resolution information is more realistic and reliable, and the image result evaluation is improved by 10% to 50%.