Improved Face Super-Resolution Reconstruction Method Based on Nearest Feature Line Manifold Learning

Abstract

The invention discloses an improved human face super-resolution reconstruction method based on the nearest characteristic line manifold learning. On the basis of the existing human face super-resolution reconstruction method based on the nearest characteristic line manifold learning, by The situation that falls on the extrapolation line of the connection line between two sample points is distinguished, that is, when the sum of the Euclidean distances from the projection point to the two sample points is greater than the Euclidean distance between the two sample points W times, then find the sample point closer to the projection point from the two sample points to replace the projection point to form a set of points to be screened, so that the projection point is restricted so that it has a stronger correlation with the sample point, which can be largely Improve the expression ability of the newly obtained sample data to the input low-resolution image block, try to avoid introducing detail information that does not exist in the original image, and improve the reconstruction effect of the low-resolution image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an improved human face super-resolution reconstruction method based on nearest feature line manifold learning. Background technique [0002] The 2016 government work report emphasized: "Innovate the comprehensive management mechanism of social security, promote the construction of social security prevention and control system with the support of informatization, punish illegal and criminal acts in accordance with the law, severely crack down on violent and terrorist activities, and enhance the sense of security of the people"; currently Among the many security measures, video surveillance and image processing technology are playing an increasingly important role in preventing and combating crimes. However, according to statistics, the quality ratio of surveillance images obtained during the day is as high as 60%, and at night it is as high as 95%. %, therefore, how to re...

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Problems solved by technology

[0002] The 2016 government work report emphasized: "Innovate the comprehensive management mechanism of social security, promote the construction of social security prevention and control system with the support of informatization, punish illegal and criminal acts in accordance with the law, severely crack down on violent and terrorist activities, and enhance the sense of security of the people"; currently Among the many security measures, video surveillance and image processing technology are playing an increasingly important role in preventing and combating crimes. However, according to statistics, the quality ratio of surveillance images obtained during the day is as high as 60%, and at night it is as high as 95%. %, therefore, how to reconstruct a high-quality recognizable face image based on the original low-quality suspect’s face image has become an urgent need for video surveillance

[0006] Although the above method greatly expands the expression ability of sample data, it lacks the necessary constraint information when selecting the nearest neighbor projection point, and introduces detailed information that does not exist in the original image, resulting in unsatisfactory image reconstruction effect.

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