# Improving Richardson--Lucy Deconvolution with Diffusion Priors for Fluorescence Microscopy

arXiv: https://arxiv.org/abs/2606.25924 · HTML: https://mlsanity.com/paper/2606.25924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 18 of its 55 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing](https://mlsanity.com/paper/2407.01521/) (2024)
- [A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images](https://mlsanity.com/paper/1812.10366/) (2018)
- [InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences](https://mlsanity.com/paper/2503.11043/) (2025)
- [RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods](https://mlsanity.com/paper/2301.05768/) (2023)
- [Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem](https://mlsanity.com/paper/2211.12510/) (2022)
- [Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy](https://mlsanity.com/paper/2307.14243/) (2023)
- [DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing](https://mlsanity.com/paper/2511.17038/) (2025)
- [SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution](https://mlsanity.com/paper/2406.09168/) (2024)

## Abstract (arXiv)

Richardson--Lucy (RL) deconvolution improves fluorescence microscopy images by recovering details lost to diffraction. It estimates the fluorescence signal most likely to have produced the measured photon counts under a Poisson imaging model. However, deconvolution remains ill-posed, especially at low photon counts, when weak biological structures become difficult to distinguish from shot noise and the prior strongly influences the reconstruction. RL recovers structure in early iterations but can amplify noise and become unstable, while regularizers such as total variation (TV) reduce these artifacts at the cost of oversmoothing. Instead, we learn a prior from real fluorescence microscopy images and use its gradient within an inverse-problem framework. The prior is estimated using an unconditional score-based diffusion model trained on a large microscopy dataset. At each step, the learned prior guides RL toward plausible specimen structures, while RL enforces Poisson consistency with the measured counts. Across diverse biological samples and cellular morphologies, the framework reduces RL noise amplification while better preserving weak structures at low photon counts.