Local feature transformation based face super-resolution reconstruction method

Abstract

The invention relates to a local feature transformation based face super-resolution reconstruction method. The method includes: performing nonnegative matrix decomposition for a low-resolution sample library matrix so as to obtain a local feature expression of a low-resolution image; transforming local features to a global feature space by the aid of a transformation relation between the local feature expression and a sample space reconstruction coefficient; as for the inputted low-resolution image, acquiring possessed features of the low-resolution image, then transforming to a sample space so as to obtain a global feature, and using a high-resolution sample library to substitute for a low-resolution sample library so as to obtain a high-resolution image; and using the high-resolution image obtained by reconstruction as an initial value, using a maximum posterior probability frame for iterative optimization of the inputted low-resolution image so that better image reconstruction quality is obtained. A global face super-resolution algorithm based on transformation of the image local features to the global feature is provided, detail representation capability of the global face algorithm is enhanced, and objective image quality of the reconstructed high-resolution image is improved.

Description

technical field [0001] The invention relates to the field of super-resolution of human face images, in particular to a super-resolution reconstruction method of human face based on local feature conversion. Background technique [0002] In the surveillance video, due to factors such as the long distance between the target and the camera, environmental noise, and imaging blur, the imaging quality of the target face image is low, the resolution of the target face is small, and there is insufficient local detail information of the face. It makes it difficult to directly identify the target face image. The learning-based face super-resolution algorithm uses the prior knowledge of the sample library to reconstruct a high-resolution face image from the input low-resolution face image under the guidance of the regression relationship between the high- and low-resolution samples of the training sample library. The learning-based face super-resolution algorithm can obtain a larger m...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a face super-resolution reconstruction method based on local feature transformation, to solve the problem of lack of local detail information and the overall characteristics of the sample database in the existing similar global face algorithms

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