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.

我们提出了一种光信号处理方案，在检测之前通过适当设计的线性变换器对光信号进行空间重新分布，以增强视觉感知任务的检测噪声鲁棒性，并通过 MNIST 分类的基准测试进行了验证。这种计算优先的检测方案可以推动广泛应用于工业和国防领域的红外机器视觉技术的发展。