Fig. 1.

We present a learned sensing network (LSN), which optimizes a microscope’s illumination to improve the accuracy of automated image classification. (a) Standard optical microscope outfitted with an array of individually controllable LEDs for illumination. (b) Network training is accomplished with a large number of training image stacks, each containing $N$ uniquely illuminated images. The proposed network’s physical layer combines images within a stack via a weighted sum before classifying the result, where each weight corresponds to the relative brightness of each LED in the array. (c) After training, the physical layer returns an optimized LED illumination pattern that is displayed on the LED array to improve classification accuracies in subsequent experiments.