Abstract
Infrared image helps improve the perception capabilities of autonomous driving in complex weather conditions such as fog, rain, and low light. However, infrared image often suffers from low contrast, especially in non-heat-emitting targets like bicycles, which significantly affects the performance of downstream high-level vision tasks. Furthermore, achieving contrast enhancement without amplifying noise and losing important information remains a challenge. To address these challenges, we propose a task-oriented infrared image enhancement method. Our approach consists of two key components: layer decomposition and saliency information extraction. First, we design an layer decomposition method for infrared images, which enhances scene details while preserving dark region features, providing more features for subsequent saliency information extraction. Then, we propose a morphological reconstruction-based saliency extraction method that effectively extracts and enhances target information without amplifying noise. Our method improves the image quality for object detection and semantic segmentation tasks. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods.
Abstract (translated)
红外图像有助于提高自动驾驶在复杂天气条件（如雾、雨和低光环境）下的感知能力。然而，红外图像常常存在对比度较低的问题，尤其是在非热辐射目标（如自行车）上，这严重影响了下游高级视觉任务的性能。此外，在不放大噪声并丢失重要信息的情况下实现对比度增强仍然是一项挑战。为了解决这些挑战，我们提出了一种面向任务的红外图像增强方法。我们的方法包括两个关键组成部分：层分解和显著性信息提取。 首先，我们设计了一种针对红外图像的分层分解方法，该方法在增强场景细节的同时保持暗区特征不变，从而为后续的显著性信息提取提供更多特征。然后，我们提出了一种基于形态重构的显著性抽取方法，可以有效地提取并增强目标信息而不放大噪声。我们的方法改善了物体检测和语义分割任务中的图像质量。 广泛的实验表明，与现有最先进的方法相比，我们的方法在性能上具有优势。
URL
https://arxiv.org/abs/2506.23353
