Vision At Night: Exploring Biologically Inspired Preprocessing For Improved Robustness Via Color And Contrast Transformations

• URL: http://arxiv.org/abs/2509.24863v1
• Date: Mon, 29 Sep 2025 14:48:32 GMT
• Title: Vision At Night: Exploring Biologically Inspired Preprocessing For Improved Robustness Via Color And Contrast Transformations
• Authors: Lorena Stracke, Lia Nimmermann, Shashank Agnihotri, Margret Keuper, Volker Blanz,
• Abstract summary: We explore biologically motivated input preprocessing for robust semantic segmentation.<n>By applying Difference-of-Gaussians (DoG) filtering to RGB, grayscale, and opponent-color channels, we enhance local contrast without modifying model architecture or training.<n>We show that such preprocessing maintains in-distribution performance while improving to adverse conditions like night, fog, and snow.
• Score: 18.437759539809175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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