ConvRML：基于随机多焦点透镜组的高质量无镜头成像
ConvRML: high-quality lensless imaging with random multi-focal lenslets
AI总结：
ConvRML通过随机多焦点透镜组和ConvNeXt算法提升无镜头成像质量，实现6.68 dB信噪比提升，并通过标准化系统验证测量性能。
摘要：
基于掩膜的无镜头成像器利用简单的光学和计算重建技术设计紧凑型因子相机，具有压缩成像能力。然而，这些成像器通常存在重建质量差的问题。本文描述了硬件和软件方面的几个进展，从而提高了无镜头成像的质量。首先，我们使用精密制造的随机多焦点透镜组（RML）相位掩膜，以产生改进的测量结果并减少复用。其次，我们实现了基于ConvNeXt的重建架构，该架构在峰值信噪比方面比最先进的注意力架构提高了6.68 dB。最后，我们建立了并行成像设置，同时使用RML、扩散器和镜头系统成像场景，收集了每个系统100,000次测量数据，用于重建模型的训练和评估。使用这种标准化系统，我们通过调制传递函数和互信息量化了RML相比扩散器的改进测量质量。我们的ConvRML系统受益于本文中提出的光学和计算发展，我们的贡献为继续开发高质量、紧凑型和压缩的无镜头成像器提供了资源。
英文摘要：
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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