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DA-CycleGAN: Degradation-Adaptive Unpaired Super-Resolution for Historical Image Restoration.

Lujun Zhai1, Yonghui Wang2, Yu Zhou1

  • 1Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX 77446, USA.

Journal of Imaging
|April 27, 2026
PubMed
Summary

This study introduces DA-CycleGAN, a novel method for enhancing historical image resolution. It effectively restores clarity to blurry, noisy old photographs using a degradation-adaptive module and a new dataset.

Keywords:
historical image restorationsuper-resolutionunpaired learning

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Area of Science:

  • Computer Vision
  • Image Restoration
  • Digital Heritage

Background:

  • Historical images are crucial for understanding the past but often suffer from severe degradation due to early camera technology.
  • Existing super-resolution (SR) models struggle with historical images due to unique, complex degradations not found in modern digital imagery.

Purpose of the Study:

  • To develop an effective super-resolution method specifically for historical image restoration.
  • To address the performance gap of current SR models when applied to degraded historical photographs.

Main Methods:

  • Proposed DA-CycleGAN, an unsupervised learning method built upon CycleGAN.
  • Introduced a degradation-adaptive (DA) module to learn and adapt to diverse, unknown degradations.
  • Collected a novel dataset of 10,000 low-resolution images from real historical films.

Main Results:

  • DA-CycleGAN demonstrated superior performance in super-resolving historical images compared to existing methods.
  • The developed image dataset proved effective for training and evaluating historical image super-resolution techniques.
  • The degradation-adaptive module successfully handled various natural degradations present in historical films.

Conclusions:

  • DA-CycleGAN offers a significant advancement in historical image restoration.
  • The new dataset and method provide valuable tools for preserving and enhancing historical visual records.