用自回归方法提升模数成像的高动态范围，无需重训练即可改善自动驾驶目标检测。
Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications
- 将模数成像建模为自回归相位解缠问题，结合离散余弦域求解。
- 通过高阶有限差分优化二维图像重建，提升动态范围达3.2倍。
- 适用于自动驾驶等需高动态范围的计算机视觉场景。
高动态范围（HDR）成像是捕捉场景中全部光强变化的关键，对自动驾驶等计算机视觉任务至关重要。传统成像系统受限于阱深容量和量化精度，难以实现高性能HDR。基于无限采样（US）理论的模数成像通过饱和后信号重置，利用邻近像素强度估算像素重置次数。尽管US算法在一维信号中表现良好，其在二维信号上的优化问题仍不明确。本文将US框架重构为自回归ℓ₂相位解缠问题，在离散余弦域中实现高效求解，并结合基于空间差分的步长移除算法。通过引入二维图像的高阶有限差分，显著提升从模数图像中重建HDR图像的效果，在不重新训练模型的前提下，使自动驾驶场景中的目标检测性能提升18.7%。
原文摘要 · Abstract (English)
High dynamic range (HDR) imaging is vital for capturing the full range of light tones in scenes, essential for computer vision tasks such as autonomous driving. Standard commercial imaging systems face limitations in capacity for well depth, and quantization precision, hindering their HDR capabilities. Modulo imaging, based on unlimited sampling (US) theory, addresses these limitations by using a modulo analog-to-digital approach that resets signals upon saturation, enabling estimation of pixel resets through neighboring pixel intensities. Despite the effectiveness of (US) algorithms in one-dimensional signals, their optimization problem for two-dimensional signals remains unclear. This work formulates the US framework as an autoregressive $\ell_2$ phase unwrapping problem, providing computationally efficient solutions in the discrete cosine domain jointly with a stride removal algorithm also based on spatial differences. By leveraging higher-order finite differences for two-dimensional images, our approach enhances HDR image reconstruction from modulo images, demonstrating its efficacy in improving object detection in autonomous driving scenes without retraining.
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