# PCA-Guided Autoencoding for Structured Dimensionality Reduction in Active Infrared Thermography

arXiv: https://arxiv.org/abs/2508.07773 · HTML: https://mlsanity.com/paper/2508.07773/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 40 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [Multi-Modal Attention Networks for Enhanced Segmentation and Depth Estimation of Subsurface Defects in Pulse Thermography](https://mlsanity.com/paper/2501.09994/) (2025)
- [Nondestructive Testing of Composite Fibre Materials with Hyperspectral Imaging : Evaluative Studies in the EU H2020 FibreEUse Project](https://mlsanity.com/paper/2111.03443/) (2021)

## Built on by (most cited first, 2 of 2)

- [Towards Cognitive Defect Analysis in Active Infrared Thermography with Vision-Text Cues](https://mlsanity.com/paper/2603.10549/) (2026)
- [Vision-Language Based Expert Reporting for Painting Authentication and Defect Detection](https://mlsanity.com/paper/2603.13437/) (2026)

## Abstract (arXiv)

Active Infrared thermography (AIRT) is a widely adopted non-destructive testing (NDT) technique for detecting subsurface anomalies in industrial components. Due to the high dimensionality of AIRT data, current approaches employ non-linear autoencoders (AEs) for dimensionality reduction. However, the latent space learned by AIRT AEs lacks structure, limiting their effectiveness in downstream defect characterization tasks. To address this limitation, this paper proposes a principal component analysis guided (PCA-guided) autoencoding framework for structured dimensionality reduction to capture intricate, non-linear features in thermographic signals while enforcing a structured latent space. A novel loss function, PCA distillation loss, is introduced to guide AIRT AEs to align the latent representation with structured PCA components while capturing the intricate, non-linear patterns in thermographic signals. To evaluate the utility of the learned, structured latent space, we propose a neural network-based evaluation metric that assesses its suitability for defect characterization. Experimental results show that the proposed PCA-guided AE outperforms state-of-the-art dimensionality reduction methods on PVC, CFRP, and PLA samples in terms of contrast, signal-to-noise ratio (SNR), and neural network-based metrics.