Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders

• URL: http://arxiv.org/abs/2412.16200v1
• Date: Mon, 16 Dec 2024 23:55:08 GMT
• Title: Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders
• Authors: Seyfal Sultanov, James P Buban, Robert F Klie,
• Abstract summary: We introduce a Three-Dimensional Convolutional Variational Autoencoder (3D-CVAE) for automated anomaly detection in EELS-SI data. By employing negative log-likelihood loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect-free material. Our results show that 3D-CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes.
• Score: 0.0
• License:
• Abstract: We introduce a Three-Dimensional Convolutional Variational Autoencoder (3D-CVAE) for automated anomaly detection in Electron Energy Loss Spectroscopy Spectrum Imaging (EELS-SI) data. Our approach leverages the full three-dimensional structure of EELS-SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing negative log-likelihood loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect-free material. In exploring methods for anomaly detection, we evaluated both our 3D-CVAE approach and Principal Component Analysis (PCA), testing their performance using Fe L-edge peak shifts designed to simulate material defects. Our results show that 3D-CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between normal and anomalous spectra, enabling reliable classification. Further analysis verifies that lower dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise-dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS-SI data, particularly valuable for analyzing complex material systems.

Related papers

• Anomaly Detection via Autoencoder Composite Features and NCE [1.2891210250935148]
  Autoencoders (AEs) or generative models are often employed to model the data distribution of normal inputs. We propose a decoupled training approach for anomaly detection that both an AE and a likelihood model trained with noise contrastive estimation (NCE)
  arXiv  Detail & Related papers  (2025-02-04T01:29:22Z)
• Adaptive Signal Analysis for Automated Subsurface Defect Detection Using Impact Echo in Concrete Slabs [0.0]
  This pilot study presents a novel, automated, and scalable methodology for detecting subsurface defect-prone regions in concrete slabs. The approach integrates advanced signal processing, clustering, and visual analytics to identify subsurface anomalies. The results demonstrate the robustness of the methodology, consistently identifying defect-prone areas with minimal false positives and few missed defects.
  arXiv  Detail & Related papers  (2024-12-23T20:05:53Z)
• DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection [5.062312533373299]
  We propose a novel dual-modality augmentation method for 3D anomaly synthesis. We introduce a reconstruction-based discriminative anomaly detection network. Our method outperforms the state-of-the-art methods on detection precision and achieves competitive segmentation performance.
  arXiv  Detail & Related papers  (2024-10-13T12:38:16Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• 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)
• Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging [29.135848304404533]
  coded aperture snapshot spectral imaging (CASSI) is proposed. core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. We propose a Residual Degradation Learning Unfolding Framework (RDLUF) which bridges the gap between the sensing matrix and the degradation process.
  arXiv  Detail & Related papers  (2022-11-13T12:31:49Z)
• A spectral-spatial fusion anomaly detection method for hyperspectral imagery [7.155465756606866]
  spectral fusion anomaly detection (SSFAD) method is proposed for hyperspectral imagery. New detector is designed to extract the local similarity spatial features of patch images in spatial domain.
  arXiv  Detail & Related papers  (2022-02-24T03:54:48Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition [78.37855832568569]
  We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function. We show numerical and experimental results for spectral CT materials decomposition.
  arXiv  Detail & Related papers  (2021-03-25T15:20:10Z)

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.