Driving scene image Dehazing model based on multi-branch and multi-scale feature fusion
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel image dehazing model for clear visuals in autonomous driving and surveillance. The efficient network effectively removes haze by fusing spatial and frequency information, enhancing real-time perception.
Area Of Science
- Computer Vision
- Image Processing
- Artificial Intelligence
Background
- Image dehazing is crucial for improving visual quality in critical applications like autonomous driving and surveillance.
- Existing methods often struggle with complex scenes and real-time processing demands.
Purpose Of The Study
- To develop an innovative and efficient image dehazing model for enhanced visual perception.
- To address limitations in current dehazing techniques, particularly for real-world driving scenarios.
Main Methods
- A multi-branch and multi-scale feature fusion network combining local and global features using depthwise separable convolutions and state space models (Mamba).
- Integration of spatial and frequency domain information via convolutional layers and Fourier transforms.
- A feature fusion mechanism employing channel attention and residual connections for dynamic feature weighting.
Main Results
- The proposed model achieves superior dehazing performance on real-world foggy driving datasets.
- Demonstrates high processing efficiency with a frame rate of 32.41 FPS and 67.96 MPx/s.
- Successfully combines Mamba and convolution for efficient global feature extraction in driving scenes.
Conclusions
- The novel model offers reliable visual perception and improved adaptability in complex environments.
- The fusion of spatial, frequency, and advanced network architectures enables effective and efficient haze removal.
- This work represents a significant advancement in real-time image dehazing for autonomous systems.
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