In the context of visual perception, the optical signal from a scene is transferred into the electronic domain by detectors in the form of image data, which are then processed for the extraction of visual information. In noisy and weak-signal environments such as thermal imaging for night vision applications, however, the performance of neural computing tasks faces a significant bottleneck due to the inherent degradation of data quality upon noisy detection. Here, we propose a concept of optical signal processing before detection to address this issue. We demonstrate that spatially redistributing optical signals through a properly designed linear transformer can enhance the detection noise resilience of visual perception tasks, as benchmarked with the MNIST classification. Our idea is supported by a quantitative analysis detailing the relationship between signal concentration and noise robustness, as well as its practical implementation in an incoherent imaging system. This compute-first detection scheme can pave the way for advancing infrared machine vision technologies widely used for industrial and defense applications.

翻译：在视觉感知任务中，场景的光学信号通过探测器以图像数据形式转换为电子域信号，再经处理提取视觉信息。然而，在夜视应用的热成像等噪声和弱信号环境中，由于噪声检测导致的数据质量固有退化，神经计算任务的性能面临重大瓶颈。本文提出一种检测前光学信号处理的概念以解决该问题。我们证明，通过精心设计的线性变换器对光学信号进行空间重分布，可以增强视觉感知任务对检测噪声的鲁棒性（以MNIST分类为基准验证）。该观点得到定量分析的支持，详细阐述了信号集中度与噪声鲁棒性之间的关系，并在非相干成像系统中实现了实际应用。这种先计算再检测的方案可为工业与国防领域广泛使用的红外机器视觉技术发展铺平道路。