# Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey

arXiv: https://arxiv.org/abs/2509.15363 · HTML: https://mlsanity.com/paper/2509.15363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Accurate Image Restoration with Attention Retractable Transformer](https://mlsanity.com/paper/2210.01427/) (2022)
- [A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images](https://mlsanity.com/paper/1812.10366/) (2018)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation](https://mlsanity.com/paper/1801.07198/) (2018)
- [CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry](https://mlsanity.com/paper/1908.09414/) (2019)
- [Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks](https://mlsanity.com/paper/2001.10830/) (2020)
- [A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems](https://mlsanity.com/paper/1910.03928/) (2019)
- [Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy](https://mlsanity.com/paper/2010.04011/) (2020)
- [Blind deblurring for microscopic pathology images using deep learning networks](https://mlsanity.com/paper/2011.11879/) (2020)
- [Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks](https://mlsanity.com/paper/1904.09974/) (2019)
- [Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network](https://mlsanity.com/paper/2105.00273/) (2021)
- [Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution](https://mlsanity.com/paper/1901.06405/) (2019)
- [Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution](https://mlsanity.com/paper/2111.09103/) (2021)
- [Estimation of Optical Aberrations in 3D Microscopic Bioimages](https://mlsanity.com/paper/2209.07911/) (2022)
- [Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration](https://mlsanity.com/paper/2310.02097/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Scale-adaptive Vision Model Links C. elegans Neuronal Morphology to Behavior for Neurotoxicity Assessment](https://mlsanity.com/paper/2607.23183/) (2026)

## Abstract (arXiv)

Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods. This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions. The core discussions take place around the key domains of microscopy image enhancement of super-resolution, reconstruction, and denoising, with each domain explored in terms of its current trends and their practical utility of deep learning.