Bijective-constrained cycle-consistent deep learning for optics-free imaging and classification

Many deep learning approaches to solve computational imaging problems have proven successful through relying solely on the data. However, when applied to the raw output of a bare (optics-free) image sensor, these methods fail to reconstruct target images that are structurally diverse. In this work we propose a self-consistent supervised model that learns not only the inverse, but also the forward model to better constrain the predictions through encouraging the network to model the ideal bijective imaging system. To do this, we employ cycle consistency alongside traditional reconstruction losses, both of which we show are needed for incoherent optics-free image reconstruction. By eliminating all optics, we demonstrate imaging with the thinnest camera possible.