The invention discloses a super-resolution reconstruction algorithm based on improved neighborhood embedding and structure self-similarity, comprising the following steps: first, the neighborhood embedding method is improved by use of structure similarity, more accurate high-frequency initial estimation is obtained, and an initial estimation algorithm based on neighborhood embedding is realized; and then, the local self-similarity and multi-scale structure similarity of a low-resolution image are used to construct a reconstruction constraint for the purpose of reconstructing high resolution, and a sparse representation dictionary is established. Compared with the prior art, on the basis that the algorithm put forward by the invention solves the problem that the learning-based super-resolution reconstruction algorithm of predecessors needs a lot of training sets, the neighborhood embedding method is improved, the method is adopted to solve the problem of inaccurate high-frequency initial estimation in a super-resolution algorithm based on local self-similarity and multi-scale similarity, and the super-resolution reconstruction effect of images is enhanced; and the saw-tooth effect and the ringing effect are suppressed better, and a reconstructed high-resolution image is closer to the real image and is of better subjective and objective quality.