Generative-adversarial-network-based blurred face reconstruction method and system

Abstract

The invention discloses a generative-adversarial-network-based blurred face reconstruction method and system. The face reconstruction method comprises: acquiring a training sample and to-be-re-reconstructed data; carrying out generative adversarial network training on two convolutional neural networks by the training sample to obtain an optimized-target-based generative network model; and acquiring a blurred face from the to-be-re-reconstructed data, inputting the blurred face into the generative network model to carry out de-blurring reconstruction of the face, thereby obtaining a clear face.The face reconstruction method and the system have the following beneficial effects: a phenomenon that the image obtained by using an interpolation method is too smooth is avoided; the image recovered by the model becomes clear and more high-frequency details are provided; because no frequency domain method is needed, a phenomenon that the frequency-domain data are lack of correlation is avoided;and the blurred face or shielded face can be reconstructed rapidly and the definition and similarity are high. In addition, the face reconstruction system is composed of an acquisition unit, a modelgeneration unit, and a face reconstruction unit; and the face reconstruction system has the same beneficial effects as the face reconstruction method.

Description

technical field [0001] The invention relates to the field of face deblurring and reconstruction, in particular to a fuzzy face reconstruction method and system based on a generative confrontation network. Background technique [0002] With the development of digital technology, people's requirements for picture clarity are getting higher and higher. The traditional approach is to choose a high-definition, high-resolution camera to take high-definition photos. However, this does not solve the problem of converting blurred pictures into high-definition pictures. Therefore, the method of deblurring face reconstruction should be used And born. [0003] The methods for deblurring face reconstruction in the prior art include: a reconstruction method based on interpolation, a reconstruction method based on learning, and a frequency domain reconstruction method. [0004] Interpolation methods such as the nearest element method, bilinear interpolation method and cubic interpolation...

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Problems solved by technology

[0004] Interpolation methods such as the nearest element method, bilinear interpolation method and cubic interpolation method, etc., the above method for the value of each pixel on the image is calculated and approximated by several points around it, the disadvantage is that the obtained image is too large Smooth, lost a lot of high frequency detail

Learning-based method: A large number of high-definition images are used to construct a learning library to generate a learning model, and the prior knowledge obtained by the learning model is introduced in the process of restoring the blurred image to obtain high-frequency details of the image; the disadvantage is that only using Image surface features

The reconstruction method based on the frequency domain has the disadvantage that the data in the frequency domain lacks correlation

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