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.

本研究解决了无镜头成像中的一个关键问题，即外部照明干扰对图像恢复的影响。通过结合基于物理的重构与深度学习方法，我们提出了多种恢复技术，以改善在各种照明条件下的图像质量。结果表明，与标准重构方法相比，我们的方法在定性和定量上都取得了显著的改善，具有广泛的实际应用潜力。

光明降临：外部照明下的稳健无镜头成像与深度学习