Seeing in the Dark: Synthesizing Underexposure for More Robust Underwater Image Augmentation

Document Type

Conference Proceeding

Publication Date

1-1-2026

Abstract

Developing computer vision models for underwater environments is challenging due to the scarcity of high-quality annotated data, particularly for degraded conditions such as severe underexposure. In this work, we propose a progressive framework for synthetically generating realistic underexposed data to bridge this domain gap. We first introduce a rigorous multi-metric curation strategy to establish a ground-truth reference dataset of real underexposed images. We then propose three degradation modeling approaches, varying in complexity: RGB-Based Global Gamma Matching (GGM), Decorrelated Luminance Matching (DLM), and Perceptual Deep Feature Optimization (PDFO). The first two proposed methods are based on histogram alignment in different color spaces, while Our final proposed method, PDFO, leverages a pre-trained deep network to optimize for perceptual similarity. Qualitative and quantitative evaluations demonstrate that PDFO outperforms baseline methods generating the closest luminance distribution to real-world data. This framework provides a robust tool for augmenting underwater datasets, enabling more reliable object detection in low-light conditions.

Publication Title

Proceedings 2026 IEEE Cvf Winter Conference on Applications of Computer Vision Workshops Wacvw 2026

ISBN

[9798331591496]

Link to Full Text

Share

COinS