Frequency domain decomposition network for optical remote sensing image destriping
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel network for optical remote sensing image destriping, effectively removing stripe noise by integrating spatial and frequency domain analysis. The method enhances target detection and recognition by preserving image details.
Area Of Science
- Remote Sensing
- Image Processing
- Computer Vision
Background
- Optical remote sensing images suffer from stripe noise due to imaging limitations.
- This noise degrades performance in subsequent tasks like target detection and recognition.
- Frequency domain analysis offers advantages for feature extraction compared to spatial domain methods.
Purpose Of The Study
- To develop an effective optical remote sensing image destriping network.
- To leverage both spatial and frequency domain features for improved noise removal.
- To enhance the accuracy of target detection and recognition by preserving image details.
Main Methods
- A frequency domain decomposition-based network is proposed.
- Wavelet decomposition and singular value decomposition are employed for feature extraction.
- Spatial-frequency coupling and multi-scale adaptive fusion blocks are utilized to enhance feature transmission.
Main Results
- The proposed method effectively distinguishes stripe and background features.
- Stripe information is accurately extracted from high-frequency components.
- The network successfully exploits the interaction between spatial and frequency domains.
Conclusions
- The developed destriping network outperforms existing state-of-the-art methods.
- The method demonstrates superior performance in both simulated and real-world experiments.
- It achieves excellent stripe noise removal while preserving crucial image details.
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