# Implicit neural image field for biological microscopy image compression

## TL;DR

This paper introduces a new AI-based method for compressing large biological microscopy images while preserving important details for scientific analysis.

## Contribution

The novel contribution is an adaptive compression workflow using implicit neural representation for bioimages.

## Key findings

- The workflow achieves high, controllable compression ratios on real-world microscopy images.
- It preserves critical details necessary for downstream scientific analysis.
- The method supports arbitrary pixel-wise decompression and varying image dimensionalities.

## Abstract

The rapid pace of innovation in biological microscopy has produced increasingly large images, putting pressure on data storage and impeding efficient data sharing, management and visualization. This trend necessitates new, efficient compression solutions, as traditional coder–decoder methods often struggle with the diversity of bioimages, leading to suboptimal results. Here we show an adaptive compression workflow based on implicit neural representation that addresses these challenges. Our approach enables application-specific compression, supports images of varying dimensionality and allows arbitrary pixel-wise decompression. On a wide range of real-world microscopy images, we demonstrate that our workflow achieves high, controllable compression ratios while preserving the critical details necessary for downstream scientific analysis.

This study presents a flexible AI-based method for compressing microscopy images, achieving high compression while preserving details critical for analysis, with support for task-specific optimization and arbitrary-resolution decompression.