Face super-resolution reconstruction method based on feature transformation based on nearest feature line

A recent feature line and feature conversion technology, applied in the field of image processing, can solve the problems of training sample library size limitation, inability to meet noise robustness, and unsatisfactory reconstruction effect

CN107481196BActive Publication Date: 2020-05-19HENAN UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2020-05-19

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Abstract

The invention discloses a nearest feature line based feature conversion face super-resolution reconstruction method, which is characterized in that face super-resolution reconstruction is performed by integrating a nearest feature line manifold learning based face super-resolution algorithm and a local feature conversion based face super-resolution algorithm, so that the method disclosed by the invention applies an idea of feature conversion compared with the nearest feature line manifold learning based face super-resolution algorithm, can also maintain most of the original information of an image while removing a lot of noise interference, and is mainly represented as better robustness to the noise; and the method improves the expression ability of a training sample library due to adoption of an idea of the nearest feature line compared with the local feature conversion based face super-resolution algorithm, enables a reconstructed target image to have better high-frequency local detail information and achieves an excellent image reconstruction effect.
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Description

technical field

[0001] The invention relates to the technical field of image processing, in particular to a face super-resolution reconstruction method based on feature transformation based on nearest feature lines. Background technique

[0002] Face super-resolution reconstruction is a method based on the observed low-resolution face image, using high-resolution image training library samples and low-resolution face image training library samples, to reconstruct the low-resolution face image to be reconstructed The most similar high-resolution face image; it can reproduce the local details of the face, achieve the purpose of enhancing the accuracy of face recognition, help improve the detection rate of public security organs, and protect the lives and property of the people.

[0003] The face super-resolution algorithm based on local feature transformation introduces the idea of ​​partial face into the feature transformation super-resolution method, which improves the image...

Examples

Embodiment Construction

[0051] The technical solution of the present invention can adopt the form of software to realize automatic process operation. The technical solution of the present invention will be further elaborated below in conjunction with the embodiments and accompanying drawings, as follows: figure 1 , figure 2 and image 3 As shown, a face super-resolution reconstruction method based on feature transformation based on the nearest feature line, specifically includes the following steps:

[0052] Step 1, input low-resolution face image x to be reconstructed, low-resolution image training sample set and high-resolution image training sample set N represents the number of training sample face patterns in the low-resolution image training sample set X and the high-resolution image training sample set Y.

[0053] In this embodiment, the FEI face database in the face super-resolution field is selected as the training sample library for the algorithm reconstruction experiment; the FEI fac...