Global Modeling Matters: A Fast, Lightweight, and Effective Baseline for Efficient Image Restoration.

Area of Science:

• Computer Vision
• Image Processing
• Deep Learning

Background:

• Adverse weather conditions degrade natural image quality, impacting downstream tasks.
• Transformer-based methods show promise but suffer from high system complexity for real-time applications.
• Existing simplifications of self-attention neglect inherent image restoration characteristics.

Purpose of the Study:

• To propose an efficient and novel image restoration baseline.
• To explore the potential of Wavelet-Fourier processing for image restoration tasks.
• To develop a network that balances restoration quality with computational efficiency.

Main Methods:

• Proposed the Pyramid Wavelet-Fourier Network (PW-FNet).
• Employed a pyramid wavelet-based multi-input multi-output structure for multi-scale and multi-frequency decomposition.
• Utilized Fourier transforms within blocks as an efficient alternative to self-attention for global modeling.

Main Results:

• PW-FNet demonstrated superior restoration quality across various tasks (deraining, deblurring, dehazing, etc.).
• Achieved significantly reduced parameter size, computational cost, and inference time compared to state-of-the-art methods.
• Outperformed existing methods in both restoration quality and efficiency.

Conclusions:

• PW-FNet offers an efficient and effective solution for real-world image restoration challenges.
• The integration of wavelet and Fourier transforms provides a powerful approach for image restoration.
• The proposed network architecture is suitable for real-time processing and deployment.