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.
We report a generative adversarial network (GAN)-based framework to super-resolve both pixel-limited and diffraction-limited images, acquired by coherent microscopy. We experimentally demonstrate a resolution enhancement factor of 2-6× for a pixel-limited imaging system and 2.5× for a diffraction-limited imaging system using lung tissue sections and Papanicolaou (Pap) smear slides. The efficacy of the technique is proven both quantitatively and qualitatively by a direct visual comparison between the network’s output images and the corresponding high-resolution images. Using this data driven technique, the resolution of coherent microscopy can be improved to substantially increase the imaging throughput.