54 citations · 70 across the 4 of their papers we have counts for
6 papers · 1 filter
CodedStereo: Learned Phase Masks for Large Depth-of-field Stereo
Shiyu Tan, Yicheng Wu, Shoou-I Yu +1
Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these vari…
Resolution enhancement in scanning electron microscopy using deep learning
Kevin de Haan, Zachary S. Ballard, Yair Rivenson +2
We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network. We demonstrate the veracity of this deep learning-based super-…
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Yichen Wu, Yair Rivenson, Hongda Wang +5
Three-dimensional (3D) fluorescence microscopy in general requires axial scanning to capture images of a sample at different planes. Here we demonstrate that a deep convolutional n…
Cross-modality deep learning brings bright-field microscopy contrast to holography
Yichen Wu, Yilin Luo, Gunvant Chaudhari +4
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artif…
Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
Yair Rivenson, Hongda Wang, Zhensong Wei +3
Histological analysis of tissue samples is one of the most widely used methods for disease diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious…
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Yichen Wu, Yair Rivenson, Yibo Zhang +4
Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s),…