Resolution enhancement in coherent imaging systems using a deep neural network

We present a super-resolution framework for coherent imaging systems using a generative adversarial network. This framework requires a single low-resolution input image, and in a single feed-forward step it performs resolution enhancement. To validate its efficacy, both a lensfree holographic imaging system with a pixel-limited resolution and a lens-based holographic imaging system with diffraction-limited resolution were used. We demonstrated that for both the pixel-limited and diffraction-limited coherent imaging systems, our method was able to effectively enhance the image resolution of the tested biological samples. This data-driven super resolution framework is broadly applicable to various coherent imaging systems.