标题： 一种在低光条件下的无透镜成像生成方法 显示英文标题

标题： A generative approach for lensless imaging in low-light conditions

摘要： 无透镜成像为传统基于镜头的系统提供了一种轻量、紧凑的替代方案，非常适合在空间受限的环境中进行探索。 然而，缺乏聚焦镜头以及这些环境中的有限光照通常会导致低光条件，其中测量结果由于光子捕获不足而受到复杂的噪声干扰。 本研究提出了一种稳健的重建方法，在低光场景中实现高质量成像，采用了两种互补的视角：模型驱动和数据驱动。 首先，我们应用物理模型驱动的视角，在测量模型的伪逆范围空间中进行重建，作为提取噪声测量中信息的第一指导。 然后，我们整合基于生成模型的视角，以抑制残余噪声作为第二指导，以抑制初始噪声结果中的噪声。 具体而言，一个可学习的维纳滤波器模块生成初始噪声重建。 然后，为了从噪声版本中快速且更重要的是稳定地生成清晰图像，我们实现了一个修改后的条件生成扩散模块。 该模块将原始图像转换到潜在小波域以提高效率，并使用修改后的双向训练过程以实现稳定化。 仿真和真实世界实验表明整体视觉质量有显著提升，推动了在具有挑战性的低光环境中无透镜成像的发展。 显示英文摘要

摘要： Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of a focusing lens and limited lighting in such environments often result in low-light conditions, where the measurements suffer from complex noise interference due to insufficient capture of photons. This study presents a robust reconstruction method for high-quality imaging in low-light scenarios, employing two complementary perspectives: model-driven and data-driven. First, we apply a physic-model-driven perspective to reconstruct in the range space of the pseudo-inverse of the measurement model as a first guidance to extract information in the noisy measurements. Then, we integrate a generative-model based perspective to suppress residual noises as the second guidance to suppress noises in the initial noisy results. Specifically, a learnable Wiener filter-based module generates an initial noisy reconstruction. Then, for fast and, more importantly, stable generation of the clear image from the noisy version, we implement a modified conditional generative diffusion module. This module converts the raw image into the latent wavelet domain for efficiency and uses modified bidirectional training processes for stabilization. Simulations and real-world experiments demonstrate substantial improvements in overall visual quality, advancing lensless imaging in challenging low-light environments.