Measurement-Adapted Eigentask Representations for Photon-Limited Optical Readout
This work addresses the problem of noisy optical sensor readout for downstream inference, offering a measurement-adapted representation that is particularly beneficial in low-light, few-shot, and high-difficulty classification scenarios.
Eigentask representations, which order readout features by noise resolvability, improve classification accuracy in photon-limited optical readout by up to 10 percentage points in few-shot MPEG-7 tasks compared to PCA and filtering-based compression.
Optical readout in low-light imaging is fundamentally limited by measurement noise, including photon shot noise, detector noise, and quantization error. In this regime, downstream inference depends not only on the optical front end, but also on how noisy high-dimensional sensor measurements are represented before classification or decision-making. Here we show that eigentasks provide a measurement-adapted representation for optical sensor outputs by ordering readout features according to their resolvability under noise. Using experimental data from a lens-based optical imaging system and a reanalysis of published data from a single-photon-detection neural network, we find that eigentask representations frequently outperform standard baselines including principal component analysis and filtering-based compression. The advantage is most pronounced in photon-limited, few-shot, and higher-difficulty classification regimes. In few-shot MPEG-7 classification, for example, the advantage over other methods reaches about 10 percentage points as the number of classes increases. In these settings, eigentasks yield more informative low-dimensional features and improve sample-efficient downstream learning. These results identify measurement-adapted representation as a promising strategy for optical inference when photon budget, acquisition time, and task complexity are constrained.