Structural Preservation Governs Data Augmentation in Deep Learning-Based Laser Speckle Material Classification

• URL: http://arxiv.org/abs/2607.22725v1
• Date: Wed, 22 Jul 2026 04:21:58 GMT
• Title: Structural Preservation Governs Data Augmentation in Deep Learning-Based Laser Speckle Material Classification
• Abstract summary: Controlled perturbations influence speckle-based material classification on the SensiCut dataset.<n>We train ResNet18 and EfficientNet-B0 under a framework comprising rotation, Gaussian blur, independent noise, spatially correlated speckle-aware noise, intensity jitter, and spatial masking.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data augmentation is routinely used to improve generalization in image classification, but the assumptions underlying standard policies are poorly matched to coherent imaging. Laser speckle patterns are not generic textures; they arise from coherent interference, and their discriminative content is carried by structured stochastic spatial and frequency statistics. This study examines how controlled augmentation perturbations influence speckle-based material classification on the SensiCut dataset. We train ResNet18 and EfficientNet-B0 under a parametric augmentation framework comprising rotation, Gaussian blur, independent Gaussian noise, spatially correlated speckle-aware noise, intensity jitter, and spatial masking, and evaluate test performance using macro F1-score averaged over three random seeds. Separate ordinary least squares models link augmentation parameters to performance for each architecture. Across both models, Gaussian blur exerts a strong negative effect (p < 0.001), indicating that low-pass filtering suppresses high-frequency structure that is informative for material discrimination. Independent pixel-wise noise is likewise harmful (p = 0.003 for EfficientNet-B0 and p = 0.001 for ResNet18), consistent with disruption of local spatial coherence. In contrast, spatially correlated perturbations yield significant positive coefficients (p = 0.004 for EfficientNet-B0 and p = 0.001 for ResNet18), showing that variability can improve robustness when it preserves speckle organization. The fitted models explain a substantial fraction of performance variation (R2 = 0.796 for EfficientNet-B0 and R2 = 0.879 for ResNet18). These results show that, in laser speckle imaging, augmentation effectiveness is determined primarily by structural preservation rather than perturbation magnitude. The findings motivate physics-aware augmentation design for coherent optical sensing.

Related papers

• Normalized Architectures are Natively 4-Bit [49.13186675123547]
  nGPT, an architecture that constrains weights and hidden representations to the unit hypersphere, is inherently more robust to low-precision arithmetic.<n>We validate this approach on both a 1.2B dense model and hybrid (Mamba-Transformer) MoE models of up to 3B/30B parameters.
  arXiv  Detail & Related papers  (2026-05-07T11:54:07Z)
• Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification [6.738210148449625]
  Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples.<n>Existing metric learning-based methods typically rely solely on spatial domain features.<n>This paper proposes the Frequency-Enhanced Dual-Subspace Network (FEDSNet)
  arXiv  Detail & Related papers  (2026-04-16T12:54:29Z)
• On the Relationship Between Representation Geometry and Generalization in Deep Neural Networks [0.0]
  We show that effective dimension -- an unsupervised geometric metric -- strongly predicts accuracy.<n>We establish causality: degrading geometry via noise causes accuracy loss (r=-0.94, $p 10(9)$), while improving geometry via PCA maintains accuracy across architectures (-0.03pp at 95% variance)<n>These results establish that effective dimension provides domain-agnostic predictive and causal information about neural network performance, computed entirely without labels.
  arXiv  Detail & Related papers  (2026-01-28T04:33:41Z)
• Zero-Shot Detection of Elastic Transient Morphology Across Physical Systems [0.0]
  We show that a representation learned from interferometric strain transients can act as a morphology-sensitive operator for unseen sensors.<n>Using a neural encoder trained exclusively on non-Gaussian instrumental glitches, we perform strict zero-shot anomaly analysis on rolling-element bearings.
  arXiv  Detail & Related papers  (2026-01-16T16:35:07Z)
• Robust Representation Consistency Model via Contrastive Denoising [83.47584074390842]
  randomized smoothing provides theoretical guarantees for certifying robustness against adversarial perturbations.<n> diffusion models have been successfully employed for randomized smoothing to purify noise-perturbed samples.<n>We reformulate the generative modeling task along the diffusion trajectories in pixel space as a discriminative task in the latent space.
  arXiv  Detail & Related papers  (2025-01-22T18:52:06Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Classification robustness to common optical aberrations [64.08840063305313]
  This paper proposes OpticsBench, a benchmark for investigating robustness to realistic, practically relevant optical blur effects. Experiments on ImageNet show that for a variety of different pre-trained DNNs, the performance varies strongly compared to disk-shaped kernels. We show on ImageNet-100 with OpticsAugment that can be increased by using optical kernels as data augmentation.
  arXiv  Detail & Related papers  (2023-08-29T08:36:00Z)
• Combining Gradients and Probabilities for Heterogeneous Approximation of Neural Networks [2.5744053804694893]
  We discuss the validity of additive Gaussian noise as a surrogate model for behavioral simulation of approximate multipliers. The amount of noise injected into the accurate computations is learned during network training using backpropagation. Our experiments show that the combination of heterogeneous approximation and neural network retraining reduces the energy consumption for variants.
  arXiv  Detail & Related papers  (2022-08-15T15:17:34Z)
• NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function [1.7188280334580193]
  NeighCNN is a deep learning-based speckle reduction algorithm that handles multiplicative noise. Various synthetic, as well as real SAR images, are used for testing the NeighCNN architecture.
  arXiv  Detail & Related papers  (2021-08-26T04:20:07Z)
• Highly Efficient Salient Object Detection with 100K Parameters [137.74898755102387]
  We propose a flexible convolutional module, namely generalized OctConv (gOctConv), to efficiently utilize both in-stage and cross-stages multi-scale features. We build an extremely light-weighted model, namely CSNet, which achieves comparable performance with about 0.2% (100k) of large models on popular object detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-12T07:00:46Z)
• Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames [146.63177174491082]
  Fuzzy $C$-Means (FCM) algorithm incorporates a morphological reconstruction operation and a tight wavelet frame transform. We present an improved FCM algorithm by imposing an $ell_0$ regularization term on the residual between the feature set and its ideal value. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.
  arXiv  Detail & Related papers  (2020-02-14T10:00:03Z)

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.