  By replacing the lens with a thin optical element, lensless imaging enables
new applications and solutions beyond those supported by traditional camera
design and post-processing, e.g. compact and lightweight form factors and
visual privacy. The latter arises from the highly multiplexed measurements of
lensless cameras, which require knowledge of the imaging system to recover a
recognizable image. In this work, we exploit this unique multiplexing property:
casting the optics as an encoder that produces learned embeddings directly at
the camera sensor. We do so in the context of image classification, where we
jointly optimize the encoder's parameters and those of an image classifier in
an end-to-end fashion. Our experiments show that jointly learning the lensless
optical encoder and the digital processing allows for lower resolution
embeddings at the sensor, and hence better privacy as it is much harder to
recover meaningful images from these measurements. Additional experiments show
that such an optimization allows for lensless measurements that are more robust
to typical real-world image transformations. While this work focuses on
classification, the proposed programmable lensless camera and end-to-end
optimization can be applied to other computational imaging tasks.
