Inspired by the human visual system's mechanisms for contrast enhancement and color-opponency, we explore biologically motivated input preprocessing for robust semantic segmentation. By applying Difference-of-Gaussians (DoG) filtering to RGB, grayscale, and opponent-color channels, we enhance local contrast without modifying model architecture or training. Evaluations on Cityscapes, ACDC, and Dark Zurich show that such preprocessing maintains in-distribution performance while improving robustness to adverse conditions like night, fog, and snow. As this processing is model-agnostic and lightweight, it holds potential for integration into imaging pipelines, enabling imaging systems to deliver task-ready, robust inputs for downstream vision models in safety-critical environments.
@article{arxiv.2509.24863,
title = {Vision At Night: Exploring Biologically Inspired Preprocessing For Improved Robustness Via Color And Contrast Transformations},
author = {Lorena Stracke and Lia Nimmermann and Shashank Agnihotri and Margret Keuper and Volker Blanz},
journal= {arXiv preprint arXiv:2509.24863},
year = {2025}
}
Comments
Accepted at the ICCV 2025 Workshop on Responsible Imaging