# V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy

arXiv: https://arxiv.org/abs/2504.07853 · HTML: https://mlsanity.com/paper/2504.07853/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction](https://mlsanity.com/paper/2007.03858/) (2020)
- [Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images](https://mlsanity.com/paper/2101.02824/) (2021)
- [FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functions](https://mlsanity.com/paper/2312.02434/) (2023)
- [NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos](https://mlsanity.com/paper/2210.12352/) (2022)
- [Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields](https://mlsanity.com/paper/2112.00002/) (2021)
- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [Disorder-invariant Implicit Neural Representation](https://mlsanity.com/paper/2304.00837/) (2023)
- [Time-Efficient Light-Field Acquisition Using Coded Aperture and Events](https://mlsanity.com/paper/2403.07244/) (2024)
- [OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation](https://mlsanity.com/paper/2203.02231/) (2022)
- [PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis](https://mlsanity.com/paper/2309.17190/) (2023)
- [MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections](https://mlsanity.com/paper/2403.10815/) (2024)
- [Acquiring a Dynamic Light Field through a Single-Shot Coded Image](https://mlsanity.com/paper/2204.12089/) (2022)
- [PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction](https://mlsanity.com/paper/2409.18223/) (2024)

## Built on by (most cited first, 2 of 2)

- [Pupil Design for Computational Wavefront Estimation](https://mlsanity.com/paper/2604.00225/) (2026)
- [Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopy](https://mlsanity.com/paper/2605.24959/) (2026)

## Abstract (arXiv)

Light field microscopy (LFM) has gained significant attention due to its ability to capture snapshot-based, large-scale 3D fluorescence images. However, existing LFM reconstruction algorithms are highly sensitive to sensor noise or require hard-to-get ground-truth annotated data for training. To address these challenges, this paper introduces V2V3D, an unsupervised view2view-based framework that establishes a new paradigm for joint optimization of image denoising and 3D reconstruction in a unified architecture. We assume that the LF images are derived from a consistent 3D signal, with the noise in each view being independent. This enables V2V3D to incorporate the principle of noise2noise for effective denoising. To enhance the recovery of high-frequency details, we propose a novel wave-optics-based feature alignment technique, which transforms the point spread function, used for forward propagation in wave optics, into convolution kernels specifically designed for feature alignment. Moreover, we introduce an LFM dataset containing LF images and their corresponding 3D intensity volumes. Extensive experiments demonstrate that our approach achieves high computational efficiency and outperforms the other state-of-the-art methods. These advancements position V2V3D as a promising solution for 3D imaging under challenging conditions.