Blog

This post introduces a novel approach to extending the depth of field (DOF) in lensless cameras using optimized radial coded masks. Our proposed method overcomes the limitations of previous and generic coded mask designs by employing a radial-shape-constrained optimization procedure, resulting in improved optical transfer functions while enabling extended DOF. Through simulations and prototype experiments, we demonstrate that our optimized radial mask achieves superior imaging quality compared to hand-crafted radial patterns and larger DOF than non-radial masks.

An occlusion-aware model for gait video processing uses SMPL-based human mesh models and machine learning to achieve superior recognition in challenging surveillance videos.

Electromagnetic inverse scattering problems (ISPs) are crucial in noninvasive imaging but challenging due to nonlinearity and computational costs. This blog explores machine learning-based ISP solvers with physics-guided loss functions, emphasizing the role of near-field priors and multiple-scattering effects. Numerical experiments highlight the advantages and limitations of these approaches.

Inspired by the capabilities of transformer models, we introduce a novel method named Multivariate Time-Series Imputation with Transformers (MTSIT). This entails an unsupervised autoencoder model featuring a transformer encoder, leveraging unlabeled observed data for simultaneous reconstruction and imputation of multivariate time-series.