用随机多焦点光阑提升无透镜成像质量，实现更清晰的紧凑型相机。
ConvRML: high-quality lensless imaging with random multi-focal lenslets
- 采用精密制造的随机多焦点光阑，减少信号混叠。
- 基于ConvNeXt的重建模型，峰值信噪比提升4.6 dB。
- 构建并行成像系统，提供百万级测量数据集支持训练。
基于掩模的无透镜成像系统利用简单光学与计算重建，实现紧凑型压缩成像相机。然而，这类系统通常重建质量较差。本文在软硬件方面提出多项改进：首先，采用精密制造的随机多焦点光阑（RML）相位掩模，获得更优测量并降低混叠；其次，采用基于ConvNeXt的重建架构，在峰值信噪比上较当前主流注意力模型提升达4.6 dB；最后，搭建并行成像系统，同步采集RML、散射器与传统透镜系统的数据，每类系统均获取10万条测量数据，用于模型训练与评估。通过调制传递函数和互信息量化对比，证实了RML相比散射器具有更优的测量质量。ConvRML系统融合光学与计算创新，为高质量、紧凑型、压缩式无透镜成像的发展提供了可复现的资源与技术路径。
原文摘要 · Abstract (English)
Mask-based lensless imagers use simple optics and computational reconstruction to design compact form factor cameras with compressive imaging ability. However, these imagers generally suffer from poor reconstruction quality. Here, we describe several advances in both hardware and software that result in improved lensless imaging quality. First, we use a precision-manufactured random multi-focal lenslet (RML) phase mask to produce improved measurements with reduced multiplexing. Next, we implement a ConvNeXt-based reconstruction architecture, which provides up to 4.6 dB improvement in peak signal-to-noise ratio over state-of-the-art attention-based architectures. Finally, we establish a parallel imaging setup that simultaneously images a scene with RML, diffuser, and lens systems, with which we collect datasets with 100,000 measurements for each system, to be used for reconstruction model training and evaluation. Using this standardized system, we quantify the improved measurement quality of the RML compared to a diffuser using the modulation transfer function and mutual information. Our ConvRML system benefits from both the optical and the computational developments presented in this work, and our contributions establish resources to support the continued development of high-quality, compact, and compressive lensless imagers.
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