🤖 AI Summary
Standard data augmentation techniques exhibit limited efficacy in laser speckle-based material classification because they overlook the structural statistical properties inherent to speckle patterns, which arise from coherent interference. This work proposes a parameterized augmentation framework to systematically evaluate the impact of various perturbations—including rotation, Gaussian blur, independent noise, spatially correlated speckle-aware noise, intensity jitter, and spatial masking—on classification performance. Leveraging ResNet18 and EfficientNet-B0 models alongside ordinary least squares analysis, the study demonstrates that augmentation effectiveness hinges on preserving the spatial and frequency-domain structure of speckle rather than the magnitude of perturbation. Structure-preserving augmentations, such as spatially correlated noise, substantially enhance robustness, whereas Gaussian blur and independent noise degrade performance. The proposed framework accounts for up to 87.9% of performance variance, establishing a design principle centered on physically informed, structure-preserving augmentation.
📝 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.