RPF: Reference-Based Progressive Face Super-Resolution Without Losing Details and Identity

Ji Soo Kim, Keunsoo Ko, Hanul Kim, Chang Su Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Face super-resolution involves generating a high-resolution facial image from a low-resolution one. It is, however, quite a difficult task when the resolution difference between input and output images is too large. In order to tackle this challenge, many approaches use generative adversarial networks that are pre-trained on a large facial image dataset, but they often generate fake details and distort the person's original face, leading to a loss of identity. Hence, in this paper, we propose a progressive face super-resolution network, called RPF, to super-resolve a facial image without losing details and personal identity by progressively exploiting the same person's high-resolution image as a reference image. First, we remove unnecessary detail information, such as hair and background, from the reference image, which may be different from the low-resolution input. Next, we align the high-resolution reference image to the low-resolution input image and blend them to generate a synthesized image. Finally, we refine the synthesized image to generate a faithful super-resolved image containing both details and identity information. Experimental results demonstrate that the proposed RPF algorithm outperforms recent state-of-the-art methods in terms of detail restoration and identity preservation, with improvements of 0.0098 and 0.0478 in LPIPS and ISC, respectively, on the CelebA-HQ dataset.

Original languageEnglish
Pages (from-to)46707-46718
Number of pages12
JournalIEEE Access
Publication statusPublished - 2023

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grants funded by the Korea Government (MSIT) under Grant NRF-2021R1A4A1031864, Grant NRF-2022R1A2B5B03002310, and Grant RS-2022-00166922.

Publisher Copyright:
© 2013 IEEE.


  • convolutional neural networks
  • Face super-resolution
  • generative adversarial networks
  • reference-based super-resolution

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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