Vision At Night: Exploring Biologically Inspired Preprocessing For Improved Robustness Via Color And Contrast Transformations
This addresses robustness issues for safety-critical vision systems, but it is incremental as it builds on existing preprocessing methods without altering model architectures.
The paper tackled the problem of improving robustness in semantic segmentation under adverse conditions by applying biologically inspired preprocessing, such as Difference-of-Gaussians filtering, to input images. The result showed maintained in-distribution performance and enhanced robustness to night, fog, and snow on datasets like Cityscapes, ACDC, and Dark Zurich.
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.