Global Search Optics (GSO) is a comprehensive end-to-end lens design framework that automatically explores the solution space to design compact computational imaging systems with superior imaging quality compared to traditional methods.
A novel Adaptive Cascade Calibrated (ACC) strategy for multi-plane phase retrieval that overcomes misalignment issues through computational self-calibration, enabling alignment-free phase imaging.
This paper introduces a novel end-to-end learning framework to optimize acquisition-time wavefront modulations, which significantly enhance the ability to recover scenes obscured by scattering media.
A phase-guided light field (PGLF) algorithm is proposed to significantly improve both the spatial and depth resolutions of 3D imaging using off-the-shelf light field cameras, overcoming the limitations of existing active light field techniques.
A novel Fourier domain algorithm is presented for efficient and stable reconstruction of high dynamic range tomographic images from modulo Radon transform measurements, providing mathematical guarantees and advantages over previous spatial domain approaches.
The core message of this work is to develop an efficient deep learning-based method for mitigating atmospheric turbulence in images and videos by carefully integrating insights from classical turbulence mitigation algorithms and leveraging a physics-grounded data synthesis approach.
Combining camera and sonar data through Gaussian splatting enables significantly better 3D geometry reconstruction and novel view synthesis compared to using camera data alone, especially in small baseline imaging scenarios.
A self-supervised method for jointly recovering an all-in-focus image and a pixel-level depth map from a single phase-coded captured image, without requiring any training dataset.
NeRT, an unsupervised and physically grounded deep learning method, can effectively mitigate various types of turbulence, including atmospheric and water turbulence, without relying on domain-specific priors or large training datasets.
Lensless imaging with rank-one projections enables cellular-scale observations using multicore fibers.