The Journal of Physical Chemistry Letters· 2026Q1
CMD-Net: A Hybrid CNN-Mamba Denoising Network for NMR Spectroscopy
- 1citations
- Q1SCImago
- 2026year
Short summary
CMD-Net, a novel hybrid CNN-Mamba network, effectively denoises 1D NMR spectra, closely matching high-SNR references and outperforming existing methods in weak signal preservation and artifact suppression.
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Key points
- CMD-Net combines CNN for local features and Mamba-2 for global context in a U-shaped encoder-decoder architecture.
- The network performs direct denoising in the frequency domain.
- Trained on synthetic 1D NMR data, it generalizes to 1D heteronuclear, 2D, and 3D NMR datasets.
- CMD-Net consistently outperforms current denoising methods in weak peak preservation and artifact suppression.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Nuclear magnetic resonance (NMR) spectroscopy is essential for chemical and biological analysis, yet its inherent insensitivity frequently yields noise-corrupted spectra that complicate weak signal detection and quantitative measurements. Here, we propose CMD-Net, a hybrid CNN-Mamba denoising network, to recover clean NMR signals closely approximating the high-SNR reference spectrum acquired with a large number of scans. The architecture integrates CNN-based local feature extraction with Mamba-2 global context modeling within a U-shaped encoder–decoder framework, enabling direct noise reduction in the frequency domain. Trained solely on synthetic 1D NMR data, CMD-Net can be applied to 1D heteronuclear spectra and 2D/3D NMR data sets. Extensive validation demonstrates that CMD-Net consistently outperforms state-of-the-art denoising methods across multiple noise levels, achieving superior weak peak preservation and artifact suppression. With a balance between performance and model size, CMD-Net holds promise for low-SNR spectral analysis and protein structural studies in chemistry, biology, materials science, and life science.
The authors' abstract, as published at the source. The Journal of Physical Chemistry Letters, 2026 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science