# Deep learning-based super-resolution in coherent imaging systems

arXiv: https://arxiv.org/abs/1810.06611 · HTML: https://mlsanity.com/paper/1810.06611/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Diffractive all-optical computing for quantitative phase imaging](https://mlsanity.com/paper/2201.08964/) (2022)
- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [Self-supervised learning of hologram reconstruction using physics consistency](https://mlsanity.com/paper/2209.08288/) (2022)
- [Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks](https://mlsanity.com/paper/2102.12281/) (2021)
- [Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning](https://mlsanity.com/paper/2207.00089/) (2022)
- [Super-Resolution for Interferometric Imaging: Model Comparisons and Performance Analysis](https://mlsanity.com/paper/2502.15397/) (2025)
- [Deep Learning Super Resolution for Satellite Cloud Mask Downscaling](https://mlsanity.com/paper/2608.24715/) (2026)

## Abstract (arXiv)

We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-limited coherent imaging systems. We experimentally validated the capabilities of this deep learning-based coherent imaging approach by super-resolving complex images acquired using a lensfree on-chip holographic microscope, the resolution of which was pixel size-limited. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics.