Structural Preservation Governs Data Augmentation in Deep Learning-Based Laser Speckle Material Classification
The study tested seven data augmentation techniques on laser speckle images to see which ones helped deep learning models classify materials better. Findings showed that blurring and random noise hurt performance because they destroy important structural patterns. However, correlated noise helped, proving that augmentation must respect the physical structure of coherent light measurements.