  Inverse problems exist in many domains such as phase imaging, image
processing, and computer vision. These problems are often solved with
application-specific algorithms, even though their nature remains the same:
mapping input image(s) to output image(s). Deep convolutional neural networks
have shown great potential for highly variable tasks across many image-based
domains, but are usually difficult to train due to their inner high
non-linearities. We propose a novel neural network architecture highlighting
fast convergence as a generic solution addressing image(s)-to-image(s) inverse
problems of different domains. Here we show that this approach is effective at
predicting phases from direct intensity measurements, imaging objects from
diffused reflections and denoising scanning transmission electron microscopy
images, with just different training datasets. This opens a way to solve
problems statistically through big data, in contrast to implementing explicit
inversion algorithms from their mathematical formulas. Previous works have
targeted much more on \textit{how} can we reconstruct rather than \textit{what}
can be reconstructed. Our strategy offers a paradigm shift.
