Denoising and Baseline Correction of Low-Scan FTIR Spectra: A Benchmark of Deep Learning Models Against Traditional Signal Processing

• URL: http://arxiv.org/abs/2601.20905v1
• Date: Wed, 28 Jan 2026 15:19:02 GMT
• Title: Denoising and Baseline Correction of Low-Scan FTIR Spectra: A Benchmark of Deep Learning Models Against Traditional Signal Processing
• Authors: Azadeh Mokari, Shravan Raghunathan, Artem Shydliukh, Oleg Ryabchykov, Christoph Krafft, Thomas Bocklitz,
• Abstract summary: We propose a physics-informed cascade Unet that separates denoising and baseline correction tasks.<n>This architecture forces the network to separate random noise from chemical signals using an embedded SNIP layer.<n>We benchmarked this approach against a standard single Unet and a traditional Savitzky-Golay/SNIP workflow.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality Fourier Transform Infrared (FTIR) imaging usually needs extensive signal averaging to reduce noise and drift which severely limits clinical speed. Deep learning can accelerate imaging by reconstructing spectra from rapid, single-scan inputs. However, separating noise and baseline drift simultaneously without ground truth is an ill-posed inverse problem. Standard black-box architectures often rely on statistical approximations that introduce spectral hallucinations or fail to generalize to unstable atmospheric conditions. To solve these issues we propose a physics-informed cascade Unet that separates denoising and baseline correction tasks using a new, deterministic Physics Bridge. This architecture forces the network to separate random noise from chemical signals using an embedded SNIP layer to enforce spectroscopic constraints instead of learning statistical approximations. We benchmarked this approach against a standard single Unet and a traditional Savitzky-Golay/SNIP workflow. We used a dataset of human hypopharyngeal carcinoma cells (FaDu). The cascade model outperformed all other methods, achieving a 51.3% reduction in RMSE compared to raw single-scan inputs, surpassing both the single Unet (40.2%) and the traditional workflow (33.7%). Peak-aware metrics show that the cascade architecture eliminates spectral hallucinations found in standard deep learning. It also preserves peak intensity with much higher fidelity than traditional smoothing. These results show that the cascade Unet is a robust solution for diagnostic-grade FTIR imaging. It enables imaging speeds 32 times faster than current methods.

Related papers

• DiffSOS: Acoustic Conditional Diffusion Model for Speed-of-Sound Reconstruction in Ultrasound Computed Tomography [2.6915545700357986]
  We propose DiffSOS, a conditional diffusion model that directly maps acoustic waveforms to SoS maps.<n>Our framework employs a specialized acoustic ControlNet to ground the denoising process in physical wave measurements.<n>We exploit the generative nature of our framework to estimate pixel-wise uncertainty, providing a measure of reliability.
  arXiv  Detail & Related papers  (2026-02-27T23:51:16Z)
• Towards Frequency-Adaptive Learning for SAR Despeckling [10.764049665817629]
  We propose a frequency-adaptive heterogeneous despeckling model based on a divide-and-conquer architecture.<n>Inspired by their differing noise characteristics, we design specialized sub-networks for different frequency components.<n>For high-frequency sub-bands rich in edges and textures, we introduce an enhanced U-Net with deformable convolutions for noise suppression and enhanced features.
  arXiv  Detail & Related papers  (2025-11-08T07:08:22Z)
• Learning Multi-scale Spatial-frequency Features for Image Denoising [58.883244886588336]
  We propose a novel multi-scale adaptive dual-domain network (MADNet) for image denoising.<n>We use image pyramid inputs to restore noise-free results from low-resolution images.<n>In order to realize the interaction of high-frequency and low-frequency information, we design an adaptive spatial-frequency learning unit.
  arXiv  Detail & Related papers  (2025-06-19T13:28:09Z)
• SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models [52.40011613324083]
  Joint source-channel coding systems (DeepJSCC) have recently demonstrated remarkable performance in wireless image transmission.<n>Existing methods focus on minimizing distortion between the transmitted image and the reconstructed version at the receiver, often overlooking perceptual quality.<n>We propose SING, a novel framework that formulates the recovery of high-quality images from corrupted reconstructions as an inverse problem.
  arXiv  Detail & Related papers  (2025-03-16T12:32:11Z)
• Fast-RF-Shimming: Accelerate RF Shimming in 7T MRI using Deep Learning [25.158522994692273]
  We introduce Fast-RF-Shimming, which achieves a 5000x speed-up compared to the traditional MLS method.<n>Our technique shows a promising solution for addressing persistent inhomogeneity challenges.
  arXiv  Detail & Related papers  (2025-01-21T14:09:58Z)
• FCDM: A Physics-Guided Bidirectional Frequency Aware Convolution and Diffusion-Based Model for Sinogram Inpainting [14.043383277622874]
  Full-view sinograms require high radiation dose and long scan times.<n>Sparse-view CT alleviates this burden but yields incomplete sinograms with structured signal loss.<n>We proposemodelname, a diffusion-based framework tailored for sinograms.
  arXiv  Detail & Related papers  (2024-08-26T12:31:38Z)
• Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion [56.38386580040991]
  Consistency Trajectory Model (CTM) is a generalization of Consistency Models (CM) CTM enables the efficient combination of adversarial training and denoising score matching loss to enhance performance. Unlike CM, CTM's access to the score function can streamline the adoption of established controllable/conditional generation methods.
  arXiv  Detail & Related papers  (2023-10-01T05:07:17Z)
• Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising [9.119226249676501]
  Hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduce the noise in HSI data cubes, both model-driven and learning-based denoising algorithms have been proposed. This paper proposes a Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these issues.
  arXiv  Detail & Related papers  (2023-05-06T13:28:20Z)
• Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction [138.04956118993934]
  We propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST) CST embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing.
  arXiv  Detail & Related papers  (2022-03-09T16:17:47Z)
• Unsupervised Single Image Super-resolution Under Complex Noise [60.566471567837574]
  This paper proposes a model-based unsupervised SISR method to deal with the general SISR task with unknown degradations. The proposed method can evidently surpass the current state of the art (SotA) method (about 1dB PSNR) not only with a slighter model (0.34M vs. 2.40M) but also faster speed.
  arXiv  Detail & Related papers  (2021-07-02T11:55:40Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Sparse-View Spectral CT Reconstruction Using Deep Learning [0.283239609744735]
  We propose an approach for fast reconstruction of sparse-view spectral CT data using a U-Net convolutional neural network architecture with multi-channel input and output. Our method is fast at run-time and because the internal convolutions are shared between the channels, the computational load increases only at the first and last layers.
  arXiv  Detail & Related papers  (2020-11-30T14:36:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.