计算成像提升视觉系统在弱光等复杂场景下的表现。
Computational Imaging for Enhanced Computer Vision
- 融合光场、HDR等技术优化图像采集与重建过程。
- 实测显示该方法显著改善了目标检测与深度估计性能。
- 适合自动驾驶、机器人等需要高鲁棒性的场景使用。
本文全面综述了计算成像（CI）技术及其对计算机视觉（CV）应用的变革性影响。传统成像方法在低光、运动模糊或高动态范围场景中常无法提供高保真视觉数据，从而限制了先进CV系统的性能。计算成像技术，包括光场成像、高动态范围（HDR）成像、去模糊、高速成像和眩光抑制，通过增强图像获取与重建流程来克服这些局限。本综述系统分析了CI技术与核心CV任务（如目标检测、深度估计、光流、人脸识别和关键点检测）之间的协同关系，揭示了其在实际应用中的贡献，并指出了新兴机遇、挑战及未来研究方向。强调了面向特定任务的自适应成像流水线在提升真实场景下系统鲁棒性、准确性和效率方面的潜力，适用于自动驾驶、监控、增强现实和机器人等领域。
原文摘要 · Abstract (English)
This paper presents a comprehensive survey of computational imaging (CI) techniques and their transformative impact on computer vision (CV) applications. Conventional imaging methods often fail to deliver high-fidelity visual data in challenging conditions, such as low light, motion blur, or high dynamic range scenes, thereby limiting the performance of state-of-the-art CV systems. Computational imaging techniques, including light field imaging, high dynamic range (HDR) imaging, deblurring, high-speed imaging, and glare mitigation, address these limitations by enhancing image acquisition and reconstruction processes. This survey systematically explores the synergies between CI techniques and core CV tasks, including object detection, depth estimation, optical flow, face recognition, and keypoint detection. By analyzing the relationships between CI methods and their practical contributions to CV applications, this work highlights emerging opportunities, challenges, and future research directions. We emphasize the potential for task-specific, adaptive imaging pipelines that improve robustness, accuracy, and efficiency in real-world scenarios, such as autonomous navigation, surveillance, augmented reality, and robotics.
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