ADE-CycleGAN: A Detail Enhanced Image Dehazing CycleGAN Network
Bingnan Yan1, Zhaozhao Yang1, Huizhu Sun1
1School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China.
Sensors (Basel, Switzerland)
|March 30, 2023
Summary
This study introduces a detail-enhanced CycleGAN for image defogging, improving detail preservation. The novel approach enhances structural similarity and peak signal-to-noise ratio in dehazed images.
Area of Science:
- Computer Vision
- Deep Learning
- Image Processing
Background:
- Image defogging using deep learning faces challenges in preserving image details.
- Existing CycleGAN models struggle to retain fine image features during defogging.
Purpose of the Study:
- To propose a novel detail-enhanced CycleGAN for improved image defogging.
- To enhance the retention of image details during the defogging process.
Main Methods:
- A modified CycleGAN framework incorporating U-Net architecture for multi-scale feature extraction.
- Introduction of Dep residual blocks for deeper feature learning.
- Integration of a multi-head attention mechanism in the generator to improve feature representation.
Main Results:
- The proposed method significantly improves Structural Similarity Index Measure (SSIM) by 12.2% and Peak Signal-to-Noise Ratio (PSNR) by 8.1% compared to standard CycleGAN.
- Demonstrated superior ability in retaining image details post-defogging.
Conclusions:
- The detail-enhanced CycleGAN effectively addresses the challenge of detail loss in image defogging.
- The proposed architecture offers a significant advancement in dehazing performance and detail preservation.
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