  We propose an effective deep learning model for signal reconstruction, which
requires no signal prior, no noise model calibration, and no clean samples.
This model only assumes that the noise is independent of the measurement and
that the true signals share the same structured information. We demonstrate its
performance on a variety of real-world applications, from sub-\r{A}ngstr\"{o}m
resolution atomic images to sub-arcsecond resolution astronomy images.
