Mingyang Xie 谢铭阳

I am currently a 3rd year Ph.D. student at the University of Maryland, advised by Christopher Metzler. Previously, I obtained my Bachelor degree from Washington University in St. Louis, advised by Ulugbek Kamilov and Brendt Wohlberg.

I am broadly interested in computer vision, with a focus on computational photography, computational imaging, and low-level to mid-level vision. I am actively looking for research internships for 2025.

Publications (* denotes equal contribution)

Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats

Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats

We introduce a simple yet effective approach for separating transmitted and reflected 3D scenes by using Gaussian Splatting and unpaired flash and no-flash multi-view images.

WaveMo: Learning Wavefront Modulations to See Through Scattering

WaveMo: Learning Wavefront Modulations to See Through Scattering

Use a proxy reconstruction network to learn an optimal set of wavefront modulation patterns in an end-to-end fashion.

Snapshot High-Dynamic-Range Imaging with a Polarization Camera

Snapshot High-Dynamic-Range Imaging with a Polarization Camera

Preprint, 2023 ICCP 2023 Poster Demo

A novel single-shot HDR imaging methodology using a polarization camera, achieving 4dB improvement over software-only baselines.

NeuWS: Neural Wavefront Shaping for Guidestar-Free Imaging Through Static and Dynamic Scattering Media

NeuWS: Neural Wavefront Shaping for Guidestar-Free Imaging Through Static and Dynamic Scattering Media

Neural signal representations enable breakthroughs in correcting for severe time-varying wavefront aberrations caused by scattering media.

TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence

TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence

Training GAN only on blurry images from a single scene to recover a sharp image without estimating the blur kernels or acquiring a large labelled dataset.

CoIL: Coordinate-Based Internal Learning for Tomographic Imaging

CoIL: Coordinate-Based Internal Learning for Tomographic Imaging

Learning the computed tomography measurement field by mapping X-ray angles to corresponding pixel values using implicit neural representation.

Joint Reconstruction and Calibration Using Regularization by Denoising with Application to Computed Tomography

Joint Reconstruction and Calibration Using Regularization by Denoising with Application to Computed Tomography

A regularization by denoising approach for image reconstruction tasks where there exist parametric uncertainties in the imaging forward model.

PROVES: Establishing Image Provenance using Semantic Signatures

PROVES: Establishing Image Provenance using Semantic Signatures

Mingyang Xie, Manav Kulshrestha, Shaojie Wang, Jinghan Yang, Ayan Chakrabarti, Ning Zhang, Yevgeniy Vorobeychik

Using semantic signing for image verification against deep fake attacks.