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.