Single image super-resolution rebuilding method

Abstract

The invention discloses a single image super-resolution rebuilding method. Based on non-local similarity and a classification half-coupling dictionary learning algorithm, the method comprises a training stage and a rebuilding stage. According to the method, the half-coupling dictionary learning algorithm is used as a framework, training image block sparse domain classification based on mapping errors is introduced, and a heuristic method strategy conducted alternatively through the sparse domain classification and the half-coupling dictionary learning is adopted; sparse domain non-local similarity restriction items are introduced, structural information of training image block space is excavated in the sparse domain so as to rebuild more high-frequency details; the sparse representation algorithm based on non-local restriction is improved to meet the requirements of the half-coupling dictionary learning algorithm overall framework; further, an error compensation mechanism is introduced into the rebuilding stage to further improve the super-resolution rebuilding quality. Compared with the prior art, the method improves rebuilt texture details and forge edge and saw tooth removal, achieves good effects, and achieves best subjective visual effect in the prior art.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to the fields of medical image diagnosis, remote sensing imaging, video monitoring and the like. Background technique [0002] Single image super-resolution reconstruction is one of the research hotspots in the field of digital image processing, and it has important application value in the fields of medical imaging diagnosis, satellite remote sensing imaging, and video surveillance. At present, the learning-based super-resolution algorithm has become one of the research hotspots in the field of super-resolution in the world in recent years. The method jointly learns high-resolution and low-resolution redundant dictionaries from a set of high-resolution and low-resolution image patches, so that each image patch in the training set can be sparsely represented under the corresponding dictionary. In the process of super-resolution reconstruction, the sparse represent...

AI Technical Summary

Problems solved by technology

This toolbox is suitable for solving small and medium-scale sparse representation problems. When the training data scale is large, the training accuracy is not high, and custom regularization items cannot be added.

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