Face super-resolution reconstruction method based on K-neighboring re-recognition

Abstract

The invention discloses a face super-resolution reconstruction method based on K-neighboring re-recognition, the method comprises the following steps: respectively dividing a to-be-reconstructed low-resolution face image and sample images in a high-resolution training set and a low-resolution training set into overlapped image blocks, for the image blocks of the to-be-reconstructed low-resolution face image, according to the priority that geometrical information with high-resolution manifold is relatively credible and relatively representative, updating the recognized neighboring image by using geometrical information with low-resolution manifold and the high-resolution manifold, computing an optimal weight coefficient when the re-recognized neighboring image blocks are used for linear reconstruction, replacing the re-recognized neighboring image blocks by using one-to-one corresponding position image blocks of corresponding images in a high-resolution training set, weighting to synthesize the high-resolution image block, fusing as the high-resolution face image according to the position of a synthesized image on the face. The method has the relatively high reconstruction precision and reconstruction efficiency, and can be used for reconstructing high-quality face image.

Description

technical field [0001] The invention relates to the field of image super-resolution, in particular to a face super-resolution reconstruction method based on K-nearest neighbor re-identification. Background technique [0002] Face images, compared to other types of biometrics (such as fingerprints, iris, retina, etc.), can be obtained in a more convenient, natural, and direct manner. Because the acquisition of face images is a non-invasive way, applications based on face images have been extensively developed and researched. However, in many cases, because the distance between the camera and the face is very far, the face image captured by the video often has only tens of pixels. Because the resolution of the face image is too low, too much detail information is lost, and it is difficult for humans or machines to recognize the face captured by the surveillance camera. Therefore, face super-resolution technology, which improves the resolution of low-quality face images in su...

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Problems solved by technology

In addition, the above methods only consider one manifold space (low-resolution block manifold), ignoring geometric information is more reliable and representative of high-resolution block manifold information

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