Zhihao Xia

I am a research scientist on Marc Levoy's computational photography team at Adobe, where I work on computer vision, computational photography and machine learning.

I received my PhD from WashU advised by Ayan Chakrabarti. Prior to WashU, I got my Bachelors from School of the Gifted Young at USTC. I've also spent time at research labs at Google Research, Adobe Research, NUS and KAUST.

Internships: I'm always happy to host research interns at Adobe. If you are interested in interning with me, please send me an email describing your past experience and current research interests.

Email  /  CV  /  Google Scholar /  GitHub


My research interests include computer vision, computational photography and deep learning. I am particularly interested in the design of accurate and efficient algorithms for visual inference --- reasoning different aspects of visual appearance (geometry, light, colors, etc) from images and videos.

Handheld Multi-Frame Neural Depth Refinement
Ilya Chugunov, Yuxuan Zhang, Zhihao Xia, Xuaner (Cecilia) Zhang, Jiawen Chen, Felix Heide
CVPR, 2022   (Oral Presentation)
project page / arxiv / code

High-fidelty depth recovery for tabletop objects from a single smartphone shot.

A Dark Flash Normal Camera
Zhihao Xia, Jason Lawrence, Supreeth Achar
ICCV, 2021
project page / arxiv / video

Face surface normal and reflectance estimation under uncontrolled visible lighting with a "dark flash image".

Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
Zhihao Xia, Michaël Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti
CVPR, 2021
project page / arxiv / code / video

A neural network-based method to denoise pairs of images taken in quick succession in low-light environments, with and without a flash.

Basis Prediction Networks for Effective Burst Denoising with Large Kernels
Zhihao Xia, Federico Perazzi, Michaël Gharbi, Kalyan Sunkavalli, Ayan Chakrabarti
CVPR, 2020
project page / arxiv / code / video

A basis prediction network that predicts global basis kernels and corresponding per-pixel coefficients for burst denoising.

Generating and Exploiting Probabilistic Monocular Depth Estimates
Zhihao Xia, Patrick Sullivan, Ayan Chakrabarti
CVPR, 2020   (Oral Presentation)
project page / arxiv / code / video

A common model that is only trained once for a variety of monocular depth applications.

Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising
Zhihao Xia, Ayan Chakrabarti
WACV, 2020
project page / arxiv / code

Image denoising by using DNN to exploit self-similarity in natural images.

Training Image Estimators without Image Ground-Truth
Zhihao Xia, Ayan Chakrabarti
NeurIPS, 2019   (Spotlight)
project page / arxiv / code

Unsupervised framework for training image estimation networks from only degraded or partial measurements.

DeeReCT-PolyA: a robust and generic deep learning method for PAS identification
Zhihao Xia, Yu Li, Bin Zhang, Zhongxiao Li, Yuhui Hu, Wei Chen, Xin Gao
Bioinformatics, 2018
supplement / code

A robust deep learning model for the identification of polyadenylation signal - a critical factor for gene expression.

Efficient and accurate inversion of multiple scattering with deep learning
Yu Sun, Zhihao Xia, Ulugbek S. Kamilov
Optics Express, 2018

A deep convolutional neural network for image reconstruction under multiple light scattering.


CSE559A: Computer Vision - Fall 2018
Teaching Assistant

This webpage is cool