# Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy

arXiv: https://arxiv.org/abs/2104.09435 · HTML: https://mlsanity.com/paper/2104.09435/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Blind Super-Resolution Kernel Estimation using an Internal-GAN](https://mlsanity.com/paper/1909.06581/) (2019)
- [Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks](https://mlsanity.com/paper/1704.01510/) (2017)
- [CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry](https://mlsanity.com/paper/1908.09414/) (2019)
- [Robust Registration of Calcium Images by Learned Contrast Synthesis](https://mlsanity.com/paper/1511.01154/) (2015)

## Built on by (most cited first, 8 of 8)

- [DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images](https://mlsanity.com/paper/2306.12109/) (2023)
- [Dual-Cycle: Self-Supervised Dual-View Fluorescence Microscopy Image Reconstruction using CycleGAN](https://mlsanity.com/paper/2209.11729/) (2022)
- [Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution](https://mlsanity.com/paper/2503.02261/) (2025)
- [Computational TIRF enables optical sectioning beyond the evanescent field for widefield fluorescence microscopy](https://mlsanity.com/paper/2511.06853/) (2025)
- [MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration](https://mlsanity.com/paper/2605.08566/) (2026)
- [SkelEM: Training-Signal Decoupling of Skeleton and Diffusion for Self-supervised Axial Super-Resolution in Volume Microscopy](https://mlsanity.com/paper/2606.30012/) (2026)
- [Physics-Informed Deep Learning Model for Cross-Modality Super-Resolution in Fluorescence Microscopy](https://mlsanity.com/paper/2607.21190/) (2026)
- [Beyond Isotropic Assumptions: Continuity-Constrained Segmentation and GPU Morphometry for Nanoscale GBM Analysis](https://mlsanity.com/paper/2608.07575/) (2026)

## Abstract (arXiv)

Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution from inferior axial resolution compared to the lateral resolution. To address this problem, here we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target volume images, our method greatly reduces the effort to put into practice as the training of a network requires as little as a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in lateral image plane and low-resolution 2D images in the other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution, but also restores suppressed visual details between the imaging planes and removes imaging artifacts.