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 face image super-resolution, in particular to a face super-resolution reconstruction method based on local feature conversion. Background technique [0002] In surveillance video, due to the long distance between the target and the camera, environmental noise, imaging blur and other factors, the image quality of the target face image is low, the resolution of the target face is small, and sufficient local detail information of the face is lacking. It makes it difficult to directly distinguish the target face image. The learning-based face super-resolution algorithm uses the prior knowledge of the sample library to reconstruct the high-resolution face image from the input low-resolution face image under the guidance of the regression relationship of the high and low resolution samples in the training sample library. The face super-resolution algorithm based on learning can obtain a larger magnification while maintaining...

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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