Sample Reconstruction

Sample reconstruction aims to recover original data from incomplete or noisy measurements, a crucial task across diverse scientific fields. Current research emphasizes hybrid approaches combining deep learning's powerful pattern recognition with physics-based models' strong generalization, addressing limitations of each individual method. Prominent techniques include latent diffusion models and autoencoders, often enhanced by wavelet scattering transforms for improved feature extraction. These advancements improve accuracy and robustness in applications ranging from microscopy to anomaly detection and even planetary exploration, impacting fields requiring high-fidelity data recovery from complex or limited observations.