# Image Enhancement Thanks to Negative Grey Levels in the Logarithmic Image Processing Framework

## TL;DR

This paper introduces a new image enhancement method using negative grey levels in the LIP framework to improve low-light images in real-time with a strong physical basis.

## Contribution

The novel concept of negative grey levels in the LIP framework enables real-time enhancement of low-light images with human vision consistency.

## Key findings

- Negative grey levels in the LIP framework extend the dynamic range of low-light images to full grey scale in real-time.
- The method is reversible and generalizable to color images, aligning with human visual system characteristics.
- Applications and extensions of the method are demonstrated, showing its versatility and effectiveness.

## Abstract

The present study deals with image enhancement, which is a very common problem in image processing. This issue has been addressed in multiple works with different methods, most with the sole purpose of improving the perceived quality. Our goal is to propose an approach with a strong physical justification that can model the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. Within this model, initially dedicated to images acquired in transmission, it is possible to introduce the novel concept of negative grey levels, interpreted as light intensifiers. Such an approach permits the extension of the dynamic range of a low-light image to the full grey scale in “real-time”, which means at camera speed. In addition, this method is easily generalizable to colour images and is reversible, i.e., bijective in the mathematical sense, and can be applied to images acquired in reflection thanks to the consistency of the LIP framework with human vision. Various application examples are presented, as well as prospects for extending this work.