用深度学习提升无镜头相机在环境光下的成像稳定性。
Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning
- 结合物理模型与可学习去噪器，融合外部光照估计进行重建
- 在多种光照条件下，定量和定性指标均显著优于传统方法
- 适合需要稳定成像的便携式或低成本视觉系统研究者
无镜头相机通过将成像过程从模拟光学转移到数字后处理，打破了传统相机的设计限制。然而，其成像极易受外界干扰（如杂散光源、噪声等）影响。本文针对尚未被充分研究的外部光照问题——如环境光和直射光源——提出多种恢复方法，通过在图像重建中引入光照估计来增强鲁棒性。核心是基于物理的重建框架，结合可学习的图像恢复模块与去噪器，所有参数均使用实测数据训练。相比标准重建方法，本方案在多种光照条件下均实现显著的定性与定量提升。论文开源了代码及包含25,000组测量数据的多光照条件数据集。
原文摘要 · Abstract (English)
Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interference (other sources, noise, etc.). In this work, we address a prevalent noise source that has not been studied for lensless imaging: external illumination e.g. from ambient and direct lighting. Being robust to a variety of lighting conditions would increase the practicality and adoption of lensless imaging. To this end, we propose multiple recovery approaches that account for external illumination by incorporating its estimate into the image recovery process. At the core is a physics-based reconstruction that combines learnable image recovery and denoisers, all of whose parameters are trained using experimentally gathered data. Compared to standard reconstruction methods, our approach yields significant qualitative and quantitative improvements. We open-source our implementations and a 25K dataset of measurements under multiple lighting conditions.
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