Official reference implementation of "Self-contrastive learning enables interference-resilient and generalizable fluorescence microscopy signal detection without interference modeling."

A Python package for compressing ExaSPIM image datasets via denoising and lossy compression.

This project demonstrates using a U-Net model with PyTorch for image segmentation and denoising tasks. It effectively segments nuclei in microscopy images and removes noise from natural images.