Abstract
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 reconstruc- tion 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.
Abstract (translated)
本文全面回顾了计算成像（CI）技术及其对计算机视觉（CV）应用的变革性影响。传统成像方法在诸如低光、运动模糊或高动态范围场景等挑战条件下，往往无法提供高质量的视觉数据，从而限制了当前最先进的CV系统的表现能力。计算成像技术，包括光场成像、高动态范围（HDR）成像、去模糊处理、高速成像和消除眩光等方法，在增强图像采集和重建过程中解决了这些问题。 本文系统地探讨了CI技术和核心CV任务之间的协同效应，这些任务包括物体检测、深度估计、光学流分析、人脸识别以及关键点检测。通过分析CI方法与其在实际CV应用中的贡献关系，本研究强调了新兴的机会、挑战及未来的研究方向。我们特别关注特定任务的自适应成像管道的潜力，这将提高真实场景（如自主导航、监控、增强现实和机器人技术）中系统的鲁棒性、准确性和效率。
URL
https://arxiv.org/abs/2509.08712
