The past decade has witnessed transformative applications of deep learning in
various computational imaging, sensing and microscopy tasks. Due to the
supervised learning schemes employed, these methods mostly depend on
large-scale, diverse, and labeled training data. The acquisition and
preparation of such training image datasets are often laborious and costly,
also leading to biased estimation and limited generalization to new sample
types. Here, we report a self-supervised learning model, termed GedankenNet,
that eliminates the need for labeled or experimental training data, and
demonstrate its effectiveness and superior generalization on hologram
reconstruction tasks. Without prior knowledge about the sample types to be
imaged, the self-supervised learning model was trained using a
physics-consistency loss and artificial random images that are synthetically
generated without any experiments or resemblance to real-world samples. After
its self-supervised training, GedankenNet successfully generalized to
experimental holograms of various unseen biological samples, reconstructing the
phase and amplitude images of different types of objects using experimentally
acquired test holograms. Without access to experimental data or knowledge of
real samples of interest or their spatial features, GedankenNet’s
self-supervised learning achieved complex-valued image reconstructions that are
consistent with the Maxwell’s equations, and its output inference and object
solutions accurately represent the wave propagation in free-space. GedankenNet
framework also exhibits resilience to random, unknown perturbations in the
physical forward model, including changes in the hologram distances, pixel size
and illumination wavelength. This self-supervised learning of image
reconstruction tasks creates new opportunities for various inverse problems in
holography, microscopy and computational imaging fields.