Phase-Aware Wavelet-Based-Scattering Encoder-Decoder for Dense Predictions
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| Format: | Preprint |
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2026
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| _version_ | 1866917528130813952 |
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| author | Marrakchi, Ghassen Matei, Basarab |
| author_facet | Marrakchi, Ghassen Matei, Basarab |
| contents | 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 conduct a preliminary extensibility study on a second dense prediction task (ISIC skin lesion segmentation), with full cross-validation as ongoing work. This work advances principled wavelet-deep learning integration, showing how phase information complements scattering's stability-expressiveness trade-off in pixel-level prediction. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_24621 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Phase-Aware Wavelet-Based-Scattering Encoder-Decoder for Dense Predictions Marrakchi, Ghassen Matei, Basarab Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.10; I.4.4; I.4.6; I.5.1; I.5.2 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 conduct a preliminary extensibility study on a second dense prediction task (ISIC skin lesion segmentation), with full cross-validation as ongoing work. This work advances principled wavelet-deep learning integration, showing how phase information complements scattering's stability-expressiveness trade-off in pixel-level prediction. |
| title | Phase-Aware Wavelet-Based-Scattering Encoder-Decoder for Dense Predictions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.10; I.4.4; I.4.6; I.5.1; I.5.2 |
| url | https://arxiv.org/abs/2605.24621 |