This blog presents a single-shot multifrequency ToF imaging system that effectively handles multipath interference and delivers reliable depth sensing in complex scenes. The proposed joint hardware–software design captures multiple Fourier coefficients of the scene response function in one exposure and leverages robust algorithms to solve the inverse problem through parametric closed-form depth reconstruction.

Steganography, the practice of concealing information within digital media, is crucial for information security and forensics. Steganographic security, defined as the ability to evade detection by steganalyzers, is a key metric in modern steganography. 

Underwater images are highly susceptible to quality degradation due to light's scattering and absorption [1,2]. Unfortunately, underwater images with deteriorating quality impose many limitations in following visual perception analysis and practical underwater applications.

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.

Explore how Bayesian optimization enhances signal processing applications by providing efficient algorithm design solutions in the signal processing toolbox.

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.

Our method overcomes 3D underwater imaging challenges by offering high-frame-rate video 3D imaging (>100 fps), providing uncertainty measures for estimates, and extending applicability to various obscurant media imaging.