提升自动驾驶相机在夜间的成像质量，减少阴影干扰。
Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications
- 提出阴影侵蚀与夜间适应性处理流程，保留颜色纹理细节。
- 相比CLAHE方法，光照均匀性提升显著，视觉质量更优。
- 适合夜间自动驾驶图像增强，可提升目标检测性能。
由于在医学成像、卫星成像、自动驾驶等领域的广泛应用，基于RGB相机的图像增强备受关注。在自动驾驶中，为应对复杂光照条件，已采用多种技术提升图像质量，包括人工增亮以改善夜间可见性、光照不变成像以降低光照变化影响，以及阴影抑制以保证日间图像清晰度。本文提出一种面向自动驾驶应用的阴影侵蚀与夜间适应性图像处理流程，在保持颜色和纹理细节的同时，有效提升图像质量。该流程与广泛使用的CLAHE方法进行对比，评估指标包括光照均匀性和视觉感知质量。结果表明，该方法显著优于CLAHE，并提升了基于YOLO的可行驶区域分割算法性能。
原文摘要 · Abstract (English)
Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various techniques are used to enhance image quality under challenging lighting conditions. These include artificial augmentation to improve visibility in poor nighttime conditions, illumination-invariant imaging to reduce the impact of lighting variations, and shadow mitigation to ensure consistent image clarity in bright daylight. This paper proposes a pipeline for Shadow Erosion and Nighttime Adaptability in images for automated driving applications while preserving color and texture details. The Shadow Erosion and Nighttime Adaptability pipeline is compared to the widely used CLAHE technique and evaluated based on illumination uniformity and visual perception quality metrics. The results also demonstrate a significant improvement over CLAHE, enhancing a YOLO-based drivable area segmentation algorithm.
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