Process for reconstructing human face image super-resolution by position image block

Abstract

The invention provides a method for reconstructing face image super-resolution by utilizing position image blocks. The method comprises the following steps: dividing a low-resolution face image and face images in high-low resolution training sets into mutually overlapped image blocks; calculating the optimal value of each divided image blocks input in the low-resolution face image during the linear restoration of the position block of each sample image in the low-resolution training set; replacing the position blocks of the sample images in the low-resolution training set by the position blocks of the sample images in the high-resolution training set, which correspond to each position block of the sample images in the low-resolution training set, and compositing image blocks of high-resolution in a weighting manner; and splicing the composited image blocks of high-resolution into a whole image according to the position of the image blocks in the face image. The method which reconstructs a high-resolution image block in the same position by utilizing the image block in the same position of each sample image in a training set directly has the advantage that the manifold learning step or the feature extraction step which are common in similar algorithms are avoided, thereby greatly saving operation time, reducing complexity; and the quality of the composited high-resolution image is improved.

Description

technical field [0001] The invention relates to an image processing method, in particular to a face image super-resolution method reconstructed by using position image blocks. Background technique [0002] Super-resolution refers to reconstructing one or more frames of high-resolution images from one or more frames of low-resolution images. The super-resolution reconstruction algorithm suitable for various images is not as good as the algorithm designed for a certain type of image. [0003] In 2000, Baker of Carnegie-Mellon University in the United States (Document 1: S.Baker, T.Kanade, Hallucinating Faces, in: Proc.of Inter.Conf.on Automatic Face and GestureRecognition, Grenoble, France, 2000, pp.83- 88.) First developed a single-frame face image super-resolution algorithm (Face hallucination) based on machine learning, and selected the horizontal and vertical derivatives of the Gaussian pyramid of the face image and the Laplacian pyramid as the features of the face image ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a face image super-resolution method using position image block reconstruction. The method of the present invention solves the problem that the existing similar algorithms have a large amount of calculation and the quality of the obtained high-resolution face image is not ideal.

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