# MicroSSIM: Improved Structural Similarity for Comparing Microscopy Data

arXiv: https://arxiv.org/abs/2408.08747 · HTML: https://mlsanity.com/paper/2408.08747/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Interpretable Unsupervised Diversity Denoising and Artefact Removal](https://mlsanity.com/paper/2104.01374/) (2021)
- [N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture](https://mlsanity.com/paper/2211.08512/) (2022)
- [μSplit: efficient image decomposition for microscopy data](https://mlsanity.com/paper/2211.12872/) (2022)
- [denoiSplit: a method for joint microscopy image splitting and unsupervised denoising](https://mlsanity.com/paper/2403.11854/) (2024)

## Built on by (most cited first, 10 of 10)

- [HazeMatching: Dehazing Light Microscopy Images with Guided Conditional Flow Matching](https://mlsanity.com/paper/2506.22397/) (2025)
- [scSplit: Bringing Severity Cognizance to Image Decomposition in Fluorescence Microscopy](https://mlsanity.com/paper/2503.22983/) (2025)
- [Unpaired Image-to-Image Translation for Segmentation and Signal Unmixing](https://mlsanity.com/paper/2505.20746/) (2025)
- [Foreground-aware Virtual Staining for Accurate 3D Cell Morphological Profiling](https://mlsanity.com/paper/2507.05383/) (2025)
- [ResMatching: Noise-Resilient Computational Super-Resolution via Guided Conditional Flow Matching](https://mlsanity.com/paper/2510.26601/) (2025)
- [λSplit: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopy](https://mlsanity.com/paper/2603.23647/) (2026)
- [Self-Tuning Regularization for Image Scanning Microscopy](https://mlsanity.com/paper/2605.31426/) (2026)
- [SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling](https://mlsanity.com/paper/2607.18990/) (2026)
- [MRI Super-Resolution with RCDM/WaveMix and Task-Aware Segmentation](https://mlsanity.com/paper/2609.39083/) (2026)
- [FANVIDv2: Evaluating Video Super-Resolution by Face and Licence-Plate Recognition Under Compound Degradation](https://mlsanity.com/paper/2609.39649/) (2026)

## Abstract (arXiv)

Microscopy is routinely used to image biological structures of interest. Due to imaging constraints, acquired images, also called as micrographs, are typically low-SNR and contain noise. Over the last few years, regression-based tasks like unsupervised denoising and splitting have found utility in working with such noisy micrographs. For evaluation, Structural Similarity (SSIM) is one of the most popular measures used in the field. For such tasks, the best evaluation would be when both low-SNR noisy images and corresponding high-SNR clean images are obtained directly from a microscope. However, due to the following three peculiar properties of the microscopy data, we observe that SSIM is not well suited to this data regime: (a) high-SNR micrographs have higher intensity pixels as compared to low-SNR micrographs, (b) high-SNR micrographs have higher intensity pixels than found in natural images, images for which SSIM was developed, and (c) a digitally configurable offset is added by the detector present inside the microscope which affects the SSIM value. We show that SSIM components behave unexpectedly when the prediction generated from low-SNR input is compared with the corresponding high-SNR data. We explain this by introducing the phenomenon of saturation, where SSIM components become less sensitive to (dis)similarity between the images. We propose an intuitive way to quantify this, which explains the observed SSIM behavior. We introduce MicroSSIM, a variant of SSIM, which overcomes the above-discussed issues. We justify the soundness and utility of MicroSSIM using theoretical and empirical arguments and show the utility of MicroSSIM on two tasks: unsupervised denoising and joint image splitting with unsupervised denoising. Since our formulation can be applied to a broad family of SSIM-based measures, we also introduce MicroMS3IM, a microscopy-specific variation of MS-SSIM.