# WaveMo: Learning Wavefront Modulations to See Through Scattering

arXiv: https://arxiv.org/abs/2404.07985 · HTML: https://mlsanity.com/paper/2404.07985/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Phase Retrieval via Wirtinger Flow: Theory and Algorithms](https://mlsanity.com/paper/1407.1065/) (2014)
- [NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images](https://mlsanity.com/paper/2005.03560/) (2020)
- [Deep Optics for Monocular Depth Estimation and 3D Object Detection](https://mlsanity.com/paper/1904.08601/) (2019)
- [Deep Optics for Single-shot High-dynamic-range Imaging](https://mlsanity.com/paper/1908.00620/) (2019)
- [Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond](https://mlsanity.com/paper/2203.16931/) (2022)
- [Imaging with Equivariant Deep Learning](https://mlsanity.com/paper/2209.01725/) (2022)
- [Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media](https://mlsanity.com/paper/1806.04139/) (2018)
- [Efficient and accurate inversion of multiple scattering with deep learning](https://mlsanity.com/paper/1803.06594/) (2018)
- [Coordinate-based neural representations for computational adaptive optics in widefield microscopy](https://mlsanity.com/paper/2307.03812/) (2023)
- [TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions](https://mlsanity.com/paper/2303.17583/) (2023)
- [Adaptive 3D descattering with a dynamic synthesis network](https://mlsanity.com/paper/2107.00484/) (2021)
- [Guidestar-free image-guided wavefront-shaping](https://mlsanity.com/paper/2007.03956/) (2020)
- [Enhancing Speckle Statistics for Imaging inside Scattering Media](https://mlsanity.com/paper/2203.14214/) (2022)
- [Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson Approaches](https://mlsanity.com/paper/2308.00734/) (2023)
- [Adaptive Inverse Mapping: A Model-free Semi-supervised Learning Approach towards Robust Imaging through Dynamic Scattering Media](https://mlsanity.com/paper/2108.08364/) (2021)

## Built on by (most cited first, 3 of 3)

- [Guidestar-Free Adaptive Optics with Asymmetric Apertures](https://mlsanity.com/paper/2602.07029/) (2026)
- [High-speed Imaging through Turbulence with Event-based Light Fields](https://mlsanity.com/paper/2603.14023/) (2026)
- [Pupil Design for Computational Wavefront Estimation](https://mlsanity.com/paper/2604.00225/) (2026)

## Abstract (arXiv)

Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy. A promising strategy to overcome this challenge is wavefront modulation, which induces measurement diversity during image acquisition. Despite its importance, designing optimal wavefront modulations to image through scattering remains under-explored. This paper introduces a novel learning-based framework to address the gap. Our approach jointly optimizes wavefront modulations and a computationally lightweight feedforward "proxy" reconstruction network. This network is trained to recover scenes obscured by scattering, using measurements that are modified by these modulations. The learned modulations produced by our framework generalize effectively to unseen scattering scenarios and exhibit remarkable versatility. During deployment, the learned modulations can be decoupled from the proxy network to augment other more computationally expensive restoration algorithms. Through extensive experiments, we demonstrate our approach significantly advances the state of the art in imaging through scattering media. Our project webpage is at https://wavemo-2024.github.io/.