# Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders

arXiv: https://arxiv.org/abs/2501.14709 · HTML: https://mlsanity.com/paper/2501.14709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2025.

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

- [DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis](https://mlsanity.com/paper/2507.14038/) (2025)

## Abstract (arXiv)

We present a physics-informed deep learning framework to address common limitations in Confocal Laser Scanning Microscopy (CLSM), such as diffraction limited resolution, noise, and undersampling due to low laser power conditions. The optical system's point spread function (PSF) and common CLSM image degradation mechanisms namely photon shot noise, dark current noise, motion blur, speckle noise, and undersampling were modeled and were directly included into model architecture. The model reconstructs high fidelity images from heavily noisy inputs by using convolutional and transposed convolutional layers. Following the advances in compressed sensing, our approach significantly reduces data acquisition requirements without compromising image resolution. The proposed method was extensively evaluated on simulated CLSM images of diverse structures, including lipid droplets, neuronal networks, and fibrillar systems. Comparisons with traditional deconvolution algorithms such as Richardson-Lucy (RL), non-negative least squares (NNLS), and other methods like Total Variation (TV) regularization, Wiener filtering, and Wavelet denoising demonstrate the superiority of the network in restoring fine structural details with high fidelity. Assessment metrics like Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR), underlines that the AdaptivePhysicsAutoencoder achieved robust image enhancement across diverse CLSM conditions, helping faster acquisition, reduced photodamage, and reliable performance in low light and sparse sampling scenarios holding promise for applications in live cell imaging, dynamic biological studies, and high throughput material characterization.