A Standard Processing Pipeline for High-accuracy Measurement of Few-shot Regression on Laser Induced Breakdown Spectroscopy

• URL: http://arxiv.org/abs/2606.21960v1
• Date: Sat, 20 Jun 2026 09:19:02 GMT
• Title: A Standard Processing Pipeline for High-accuracy Measurement of Few-shot Regression on Laser Induced Breakdown Spectroscopy
• Abstract summary: Laser-induced breakdown spectroscopy (LIBS) faces challenges in high-accuracy quantitative measurement under few-shot scenarios.<n>Traditional preprocessing methods fail to preserve subtle spectral features or capture nonlinear correlations.<n>This work proposes a standardized processing pipeline integrating diffusion-based denoising, attention-based autoencoder for dimensionality reduction, group shuffling data augmentation, and ordinary least squares regression.
• Score: 6.188951069930088
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Laser-induced breakdown spectroscopy (LIBS) faces challenges in high-accuracy quantitative measurement under few-shot scenarios due to spectral noise and data scarcity. Traditional preprocessing methods often fail to preserve subtle spectral features or capture nonlinear correlations. This work proposes a standardized processing pipeline integrating diffusion-based denoising, attention-based autoencoder for dimensionality reduction, group shuffling data augmentation, and ordinary least squares regression. The diffusion module employs a 3D UNet architecture to remove spectral noise while preserving essential emission features. The attention-autoencoder captures nonlinear spectral correlations, effectively reducing high-dimensional spectral data to compact latent representations. Group shuffling data augmentation enhances model robustness by creating synthetic samples through feature group permutation. Experimental results on multiple elemental concentrations demonstrate that our Diffusion-DA-AE pipeline achieves superior performance with a mean RMAE of 0.2847, representing 37.7\% and 37.6\% improvements over baseline autoencoder and traditional PCA-PLS regression, respectively. The framework's effectiveness validates its generalizability and establishes a new benchmark for few-shot LIBS regression.

Related papers

• SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity [57.84199526590779]
  Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that hampers global convergence.<n>In this work, we revisit client drift from a novel frequency-domain perspective and uncover a critical Spectral Bias of Drift.<n>Motivated by this, we propose SpecGradFilter, a unified Spectral Filtering Gradient Framework that tames heterogeneity by suppressing discordant low-frequency signals.
  arXiv  Detail & Related papers  (2026-07-05T09:23:35Z)
• CMDS-AD: Cross-Modal Dual-Stream Decoupling for Few-Shot Anomaly Detection [22.190054508477704]
  Few-shot anomaly detection remains challenging due to limited training data.<n>We present CMDS-AD, a Cross-Modal Dual-Stream Anomaly Detection framework.<n> CMDS-AD achieves absolute performance gains of 5.7% (I-AUROC) and 2.0% (AUPRO) on MVTec 3D-AD, alongside 7.7% and 5.6% improvements on EyeCandies.
  arXiv  Detail & Related papers  (2026-06-18T14:36:02Z)
• Spectrally Regularized Latent Flow Matching for Turbulence Generation [0.0]
  We introduce a latent flow matching framework with a spectrally regularized compression stage.<n>On a 2562 DNS dataset at Re_f approx 2250, replacing an MSE-trained VAE with a zone-weighted log-spectral objective raises deep-dissipation retained spectral power.<n>The improved latent representation also yields a substantially better sampling cost-fidelity tradeoff.
  arXiv  Detail & Related papers  (2026-06-10T06:09:51Z)
• REFINE: Super-efficient 3D Gaussian Splatting Pruning via Rendering-Free Primitive Importance [58.94345609736356]
  REFINE is a highly accelerated 3DGS pruning framework centered on a novel rendering-free primitive importance metric.<n>Experiments across multiple benchmark datasets demonstrate that REFINE maintains highly competitive rendering quality.
  arXiv  Detail & Related papers  (2026-06-08T06:16:38Z)
• Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting [21.300422393514804]
  We propose a deterministic framework that reformulates nowcasting as progressive frequency-decoupled refinement.<n>SDIR first extracts a stable low-frequency synoptic skeleton, then iteratively refines high-frequency textures under physical constraints.<n> Experiments on three benchmarks show SDIR significantly outperforms SOTA methods in spatial accuracy.
  arXiv  Detail & Related papers  (2026-06-01T07:34:30Z)
• HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation [50.46910397782266]
  Hyperspectral image (HSI) restoration is crucial for reliable analysis, as real HSIs suffer from degradations like noise, blur, and resolution loss.<n>We present HIR-ALIGN, a plug-and-play target-adaptive augmentation framework that enhances hyperspectral image restoration by augmenting limited training images with synthetic data that closely matches the target distribution using no extra data.
  arXiv  Detail & Related papers  (2026-05-13T14:14:13Z)
• XANE(3): An E(3)-Equivariant Graph Neural Network for Accurate Prediction of XANES Spectra from Atomic Structures [5.177624503826816]
  XANE(3) is a physics-based E(3)-equivariant graph neural network for predicting X-ray absorption near-edge structure (XANES) spectra directly from atomic structures.<n>We evaluate the model on a dataset of 5,941 FDMNES simulations of iron oxide surface facets and obtain a spectrum mean squared error of $1.0 times 10-3$ on the test set.
  arXiv  Detail & Related papers  (2026-04-13T23:42:14Z)
• Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder [1.7789378551794652]
  We propose a data-driven framework for synthesizing in-vivo MRS data using a variational autoencoder (VAE) trained exclusively on measured single-voxel spectroscopy data.<n>The VAE learns a low-dimensional latent representation of complex-valued spectra and enables generation of new samples through latent-space sampling and synthesis.<n>The results demonstrate that the VAE accurately reconstructs dominant spectral patterns and generates synthetic spectra that occupy the same feature space as in-vivo data.
  arXiv  Detail & Related papers  (2026-02-28T16:52:16Z)
• Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation [45.717539734334906]
  Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates.<n>We show that existing approaches that optimize the high-dimensional initial noise suffer from severe inefficiency, as many search directions exert negligible influence on the final generation.<n>We propose Spectral Evolution Search (SES), a plug-and-play framework for initial noise optimization that executes gradient-free evolutionary search within a low-frequency subspace.
  arXiv  Detail & Related papers  (2026-02-03T07:19:39Z)
• CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting [57.73006852239138]
  We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS)<n>Inspired by recent advances in rate-distortion-optimized 3DGS compression, this work integrates semantic learning into the compression pipeline to support decoder-side applications.<n>Our scheme features a lightweight implicit neural representation-based hyperprior, enabling efficient entropy coding of both color and semantic attributes.
  arXiv  Detail & Related papers  (2026-01-19T08:21:45Z)
• SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification [1.2871580250533408]
  Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis.<n>Traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance.<n>To address these challenges, we propose the Self-Adaptive- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks.<n>This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-07-13T04:53:33Z)
• Metropolis-Hastings Sampling for 3D Gaussian Reconstruction [31.840492077537018]
  We propose an adaptive sampling framework for 3D Gaussian Splatting (3DGS)<n>Our framework overcomes limitations by reformulating densification and pruning as a probabilistic sampling process.<n>Our approach achieves faster convergence while matching or modestly surpassing the view-synthesis quality of state-of-the-art models.
  arXiv  Detail & Related papers  (2025-06-15T19:12:37Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification [80.93625278357229]
  spectral classification can be performed directly in the compressive domain, considering the amount of spectral information embedded in the measurements. This work proposes an end-to-end approach to jointly design the coding patterns used in CSI and the network parameters to perform spectral classification directly from the embedded near-infrared compressive measurements.
  arXiv  Detail & Related papers  (2022-05-27T15:55:53Z)

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.