# Computational Imaging for VLBI Image Reconstruction

arXiv: https://arxiv.org/abs/1512.01413 · HTML: https://mlsanity.com/paper/1512.01413/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2015.

## Built on by (most cited first, 12 of 12)

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- [Dynamic Black-hole Emission Tomography with Physics-informed Neural Fields](https://mlsanity.com/paper/2602.08029/) (2026)
- [PolarRec: Radio Interferometric Data Reconstruction with Polar Coordinate Representation](https://mlsanity.com/paper/2308.14610/) (2023)
- [VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction](https://mlsanity.com/paper/2403.00897/) (2024)
- [Sensoformer: Robust Sim-to-Real Inference on Variable-Geometry Sensor Sets via Physics-Structured Randomization](https://mlsanity.com/paper/2601.06320/) (2026)
- [VVTRec: Radio Interferometric Reconstruction through Visual and Textual Modality Enrichment](https://mlsanity.com/paper/2601.06475/) (2026)
- [U-DAVI: Uncertainty-Aware Diffusion-Prior-Based Amortized Variational Inference for Image Reconstruction](https://mlsanity.com/paper/2602.11704/) (2026)
- [FlowSGS: Improving Flow Matching Priors for Inverse Imaging with Stochastic Interpolants](https://mlsanity.com/paper/2609.20769/) (2026)

## Abstract (arXiv)

Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods often require careful tuning and parameter selection for different types of data, our method (CHIRP) produces good results under different settings such as low SNR or extended emission. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the community, and provide a dataset website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons across algorithms.