Wavelet Scattering

Wavelet scattering transforms are a signal processing technique used to extract robust and interpretable features from various data types, including images and time series, by applying a cascade of wavelet transforms, nonlinearities, and averaging operations. Current research focuses on applying wavelet scattering, often integrated with deep learning architectures like convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to improve performance in diverse applications such as image forgery detection, material classification using radar, and marine mammal vocalization recognition. This approach offers advantages in robustness to noise and variations, leading to improved accuracy and generalization in various fields, particularly those dealing with complex or noisy data.