Abstract

Extracting information from optical sensors is especially challenging under dim-light conditions. I will discuss how computing a set of transformations on raw sensor data—called Eigentasks—enables robust information extraction from noisy sensor data. I will introduce the Eigentask framework for physical neural network–based sensors operating in the shot-noise limit, and present its application in experiments using two different optical image sensors. Eigentasks here are found to yield a low-dimensional latent space that consistently outperforms standard methods like PCA and low-pass filtering. I’ll conclude with a perspective on how Eigentasks may offer a robust preprocessing solution to boost the efficiency and accuracy of machine-vision pipelines.