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WaveHiT-SR: Hierarchical Wavelet Network for Efficient Image Super-Resolution

Created by
  • Haebom

Author

Fayaz Ali, Muhammad Zawish, Steven Davy, Radu Timofte

Outline

In this paper, we propose WaveHiT-SR, a novel image super-resolution (SR) method that integrates the wavelet transform within a hierarchical transformer framework. To overcome the limited receptive range of existing transformer-based SR methods, we employ adaptive hierarchical windows instead of fixed, small windows to capture features at various levels and enhance the ability to model long-range dependencies. Furthermore, we utilize the wavelet transform to decompose images into multiple frequency bands, preserving structural details while focusing on both global and local features. Hierarchical processing allows for progressive reconstruction of high-resolution images, reducing computational complexity while minimizing performance degradation. We demonstrate the effectiveness and efficiency of WaveHiT-SR through extensive experiments, and demonstrate that improved versions of SwinIR-Light, SwinIR-NG, and SRFormer-Light achieve higher efficiency (fewer parameters, fewer FLOPs, and faster speed) and state-of-the-art SR results.

Takeaways, Limitations

Takeaways:
We demonstrate that combining adaptive hierarchical windowing with wavelet transform can improve image super-resolution performance while reducing computational complexity.
Achieves higher efficiency (fewer parameters, lower FLOPs, faster speed) than existing excellent transformer-based SR models.
Generate high-resolution images while better preserving details through feature extraction across multiple frequency bands.
Limitations:
There is a possibility that the performance of the proposed method may be biased towards certain types of images or datasets.
Performance may be affected by wavelet transform parameter settings. Further research is needed to determine optimal parameter settings.
A more comprehensive comparative analysis with other state-of-the-art SR methods is needed.
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