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EAFormer: Edge-Aware Guided Adaptive Frequency-Navigator Network for Image Restoration.

Wenjie Xie1, Dong Zhou1, Wenshuai Zhang1

  • 1Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study introduces the edge-aware guided adaptive frequency navigation network (EAFormer) for versatile image restoration. EAFormer enhances edge detail reconstruction and adaptively navigates frequency features for improved visual coherence and realism across tasks.

Keywords:
image deburringimage denoisingimage derainingimage desnowingimage restorationlow-light enhancement

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Processing

Background:

  • Existing deep learning models struggle with general image restoration due to task-specific limitations.
  • Degradation types necessitate adaptive frequency feature handling, which current networks often overlook.
  • Reconstruction of edge contours remains a challenge, leading to less defined restored images.

Purpose of the Study:

  • To develop a versatile deep learning network for general image restoration.
  • To improve the reconstruction of edge contour details in restored images.
  • To enable adaptive frequency navigation for enhanced image restoration.

Main Methods:

  • Proposed the edge-aware guided adaptive frequency navigation network (EAFormer).
  • Incorporated edge detection operators to extract and reconstruct edge contour details.
  • Implemented adaptive frequency navigation to process high- and low-frequency features interactively.

Main Results:

  • EAFormer demonstrated versatility across five classic image restoration tasks.
  • The model achieved advanced performance in restoring images with improved edge clarity.
  • Enhanced retention of global structural information and visual coherence in restored images.

Conclusions:

  • EAFormer offers a robust solution for general image restoration challenges.
  • The network effectively reconstructs edge details and preserves structural integrity.
  • Adaptive frequency navigation contributes to more visually coherent and realistic image restoration.