Low-resolution face image rebuilding method based on super-resolution rebuilding technology

Abstract

The invention relates to a low-resolution face image rebuilding method based on a super-resolution rebuilding technology and belongs to the field of image processing. The rebuilding method comprises enabling an original image to undergo normalization based on face three-point positioning, and obtaining a to-be-rebuilt normalization face image; then denoising a to-be-rebuilt low-resolution image by generating a training set, determining a learning sample by ambiguity estimation, and obtaining a finally rebuilt high-resolution image by locally linear embedding (LLE) learning. The adopted face image normalization method not only is accurate, but also is consistent with a training set normalization method, and improves accuracy of the low-resolution face image. The super-resolution face image rebuilding method based on low frequency components is adopted to manufacture large-size rebuilt image of the low-resolution face image, and rebuilding of the low-resolution face image is achieved well.

Description

technical field [0001] The invention belongs to the field of image processing, and particularly relates to a method for making a low-resolution face reconstruction image. Background technique [0002] At present, video surveillance has developed rapidly and played an increasingly important role in security work. In the video surveillance images, a lot of portrait information related to the case was recorded. However, because the face images of the involved persons in video surveillance are often very small, it is impossible to distinguish the persons involved, which has caused the handling of many cases to be in trouble. The essential problem of this small face image is the low resolution of the face. The reconstruction technology of low-resolution face images is a key technology urgently needed by the public security department in case handling. The face image referred to in the present invention is limited to a frontal face image (the same below). It includes the part ...

AI Technical Summary

Problems solved by technology

Although this method can reconstruct ultra-low resolution images, it directly performs manifold learning in the time domain, the similarity of local geometric structures is not high, and the popular learning effect is poor.

In addition, the reconstruction process is greatly affected by illumination and noise, and the ultra-low resolution face image is not registered, so the super-resolution image reconstruction effect in the actual scene is poor.

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