Super-resolution reconstruction algorithm based on improved neighborhood embedding and structure self-similarity

Abstract

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.

Description

technical field [0001] The invention relates to the field of image processing, and more specifically, to a super-resolution reconstruction algorithm based on improved neighborhood embedding and structural self-similarity. Background technique [0002] Since Tsai et al. proposed the concept of super-resolution in 1984, super-resolution reconstruction technology has received widespread attention, and many super-resolution reconstruction algorithms have been proposed. These algorithms can be roughly divided into three categories: interpolation-based methods, reconstruction-based methods, and learning-based methods. The interpolation-based method has low computational complexity and fast operation speed, but the interpolated image usually lacks high-frequency details, which is easy to cause blurred edges; the reconstruction-based method uses the degraded model of the image and specific prior knowledge for super-resolution reconstruction , the prior models commonly used in the g...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the problem of difficult isolation of DC system faults in the prior art, the present invention proposes a method that combines neighborhood embedding and structural self-similarity Effectively Combined Image Super-resolution Reconstruction Algorithm

Images