Phase-Aware Wavelet-Based-Scattering Encoder-Decoder for Dense Predictions

1. 1Build a scattering transform model to achieve Lipschitz stability and translation invariance
2. 2Apply phase-aware skip connections to preserve spatial structure
3. 3Configure the encoder-decoder architecture to restore phase information
4. 4Test the model on image denoising tasks, such as BSD68
5. 5Evaluate the performance using metrics like PSNR
6. 6Refine the model by adjusting the spatial shuffle mechanism

This technique benefits computer vision teams working on image denoising and other dense prediction tasks, as it improves the preservation of spatial structure and translation invariance

💡 Preserving phase information in skip connections is crucial for improving dense prediction tasks

🔍 Phase-aware scattering encoder-decoder improves image denoising by +3.2dB! 💻