The invention discloses a face image super-resolution
reconstruction method based on a discriminable attribute constraint
generative adversarial network, and belongs to the field of digital images / video
signal processing. The method comprises the following steps: firstly, designing a
processing flow of face detailed information enhancement; secondly, designing a
network structure according to theflow, and acquiring an HR image from an LR image through the network; and lastly, performing
face verification accuracy evaluation on the HR image through a face recognition network. Through adoptionof the method, enhancement including LR face image detailed information can be completed, and the accuracy of
face verification is increased. Secondly, the generative network completes compensation ofimage high-frequency information firstly, then completes image amplification by subpixel
convolution, and finally completes stepwise image amplification through a
cascade structure, thereby completing enhancement of image detailed information. An attribute constraint module are trained together with a
perception module and an adversarial model in order to perform fine adjustment of the performance of a network reconstructed image. Finally, a reconstructed image of the generative network is input into a
face verification network, so that the accuracy of face
verification is increased.