Human face super-resolution reconstruction method based on generative adversarial network and sub-pixel convolution

Abstract

The invention discloses a human face super-resolution reconstruction method based on a generative adversarial network and sub-pixel convolution, and the method comprises the steps: A, carrying out the preprocessing through a normally used public human face data set, and making a low-resolution human face image and a corresponding high-resolution human face image training set; B, constructing the generative adversarial network for training, adding a sub-pixel convolution to the generative adversarial network to achieve the generation of a super-resolution image and introduce a weighted type loss function comprising feature loss; C, sequentially inputting a training set obtained at step A into a generative adversarial network model for modeling training, adjusting the parameters, and achieving the convergence; D, carrying out the preprocessing of a to-be-processed low-resolution human face image, inputting the image into the generative adversarial network model, and obtaining a high-resolution image after super-resolution reconstruction. The method can achieve the generation of a corresponding high-resolution image which is clearer in human face contour, is more specific in detail and is invariable in features. The method improves the human face recognition accuracy, and is better in human face super-resolution reconstruction effect.

Description

technical field [0001] The invention relates to the field of image reconstruction methods, in particular to a face super-resolution reconstruction method based on generative confrontation network and sub-pixel convolution. Background technique [0002] In the image field, image resolution has always been one of the main technical indicators that characterize the image observation level. The resolution of an image usually refers to the spatial resolution in image processing. The higher the resolution of the image, the smaller the area of ​​the actual scene represented by a certain number of image pixels, the finer the details of the scene that the image can reflect, and the more abundant information it can provide. The image super-resolution reconstruction technology may enable the transformation of the image from the detection level to the recognition level, or further to the fine resolution level. Image super-resolution reconstruction technology can improve image recognit...

AI Technical Summary

Problems solved by technology

However, this kind of method uses the loss function of image pixel-by-pixel difference, and cannot perceive the difference information in the semantic direction of the image, especially for images with distinctive features such as faces, which cannot produce good face reconstruction effects.

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