AI BriefingImage Restoration through the Combination of Fast Fourier Transform and Transformer (2024)
Key point
Proposes SFHformer, which integrates the FFT mechanism into Transformer to enhance efficiency across various image restoration tasks.
Details
To restore image damage caused by various weather conditions or degradation mechanisms, this work proposes the SFHformer framework, which integrates the FFT (Fast Fourier Transform) mechanism into the Transformer architecture.
This model adopts a dual-domain hybrid structure combining the Spatial domain and the Frequency domain. The Spatial domain handles Local modeling, while the Frequency domain performs Global modeling to secure an efficient Receptive field.
Key technical features are as follows:
- A unique Positional coding and Frequency dynamic convolution design to extract rich features from each frequency component
- Achieving an optimal balance among performance, parameter size, and computational cost
Experimental results on 31 datasets and 10 restoration tasks (dehazing, deraining, deblurring, super-resolution, low-light enhancement, etc.) demonstrate performance surpassing existing SOTA (State-of-the-art) models.
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