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Related Experiment Video

Updated: Apr 11, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Spatial-frequency complementary fusion network for dehazing with multi-scale and attention modules.

Chenguang Yan1,2, Gang Liu3,4

  • 1College of Applied Mathematics, Chengdu University of Information Technology, Chengdu, 610225, China.

Scientific Reports
|April 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new network that combines spatial and frequency domain information for clearer images. The Spatial-Frequency Complementary fusion Network improves detail and color accuracy in single image dehazing.

Keywords:
Feature fusionImage dehazingSpatial-frequency complementary attentionSpatial-frequency multi-scale module

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Single image dehazing is an ill-posed problem crucial for image restoration.
  • Learning-based methods excel but often neglect frequency-domain information.
  • Existing methods primarily rely on spatial-domain features.

Purpose of the Study:

  • To propose a novel end-to-end Spatial-Frequency Complementary fusion Network (SFCN) for single image dehazing.
  • To address the limitation of existing methods by integrating frequency-domain information.
  • To enhance feature representation and image detail preservation.

Main Methods:

  • Developed a Spatial-Frequency Complementary fusion Network (SFCN).
  • Incorporated a Spatial-Frequency Multi-scale Module for deep feature fusion.
  • Utilized a Spatial-Frequency Complementary Attention module with adaptive gating.

Main Results:

  • The SFCN achieved competitive results on synthetic and real-world datasets.
  • Demonstrated superior performance in color fidelity.
  • Showcased enhanced detail retention compared to existing methods.

Conclusions:

  • The proposed SFCN effectively fuses spatial and frequency domain information for superior dehazing.
  • The novel modules enable efficient incorporation and optimization of frequency-domain features.
  • The method offers significant improvements in color fidelity and detail preservation for single image dehazing.