arXiv:2605.24621v1 Announce Type: cross Abstract: Scattering transforms achieve Lipschitz stability and translation invariance, but dense prediction tasks require preserving spatial structure lost in global averaging. We propose Phase-Aware Scattering Encoder-Decoder, which restores this information by explicitly preserving phase in skip connections. On image denoising (BSD68), breaking translation invariance improves PSNR by $+2.17$~dB; phase preservation adds $+1.03$~dB. A novel spatial shuffling ablation ($-1.26$~dB penalty) demonstrates phase encodes location-dependent structure. We conduc
This research builds on recent advancements in AI model efficiency and architectural innovation, seeking to improve performance in dense prediction tasks.
Improving the accuracy and stability of AI models in tasks like image denoising has broad implications for computer vision applications across industries.
The proposed Phase-Aware Scattering Encoder-Decoder offers a novel method to enhance AI model performance by better preserving spatial information, potentially leading to more robust and accurate vision systems.
- · Computer Vision Researchers
- · AI hardware manufacturers
- · Robotics companies
- · Image processing software developers
- · Legacy image processing techniques
- · AI models without phase preservation methods
Improved performance in dense prediction tasks like image denoising and segmentation.
Accelerated development and adoption of AI in fields requiring high spatial accuracy, such as autonomous vehicles and medical imaging.
Enhanced AI capabilities leading to new applications in areas like scientific discovery and industrial automation where detailed spatial understanding is critical.
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