Deep learning-based super-resolution in coherent imaging systems
This provides a rapid, non-iterative method for improving the space-bandwidth product in coherent imaging, addressing resolution limitations in optics, though it appears incremental as it applies existing GAN methods to this specific domain.
The authors tackled the problem of enhancing resolution in coherent imaging systems by developing a deep learning framework based on a generative adversarial network (GAN), which they experimentally validated on both pixel size-limited and diffraction-limited systems, achieving super-resolution in complex images from holographic microscopes.
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.