  We demonstrate that image reconstruction can be achieved via a convolutional
neural network for a "see-through" computational camera comprised of a
transparent window and a CMOS image sensor. Furthermore, we compared
classification results using a classifier network for the raw sensor data vs
the reconstructed images. The results suggest that similar classification
accuracy is likely possible in both cases with appropriate network
optimizations. All networks were trained and tested for the MNIST (6 classes),
EMNIST and the Kanji49 datasets.
