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Found 15 papers, 0 papers with code
Unidirectional imaging with partially coherent light
Here, we report unidirectional imaging under spatially partially coherent light and demonstrate high-quality imaging only in the forward direction (A->B) with high power efficiency while distorting the image formation in the backward direction (B->A) along with low power efficiency.
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration
The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, whereas their adaptation on image restoration, particularly within computational microscopy remains theoretically and empirically underexplored.
Autonomous Quality and Hallucination Assessment for Virtual Tissue Staining and Digital Pathology
Here, we present an autonomous quality and hallucination assessment method (termed AQuA), mainly designed for virtual tissue staining, while also being applicable to histochemical staining.
Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data
The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging.
Virtual histological staining of unlabeled autopsy tissue
Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time.
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems
Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers.
eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
The application of deep learning techniques has greatly enhanced holographic imaging capabilities, leading to improved phase recovery and image reconstruction.
Self-supervised learning of hologram reconstruction using physics consistency
Here, we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labeled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks.
Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope's autofocusing precision.
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging.
Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network
Here, we demonstrate a few-shot transfer learning method that helps a holographic image reconstruction deep neural network rapidly generalize to new types of samples using small datasets.
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging.
Deep learning-based virtual refocusing of images using an engineered point-spread function
We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF).
Recurrent neural network-based volumetric fluorescence microscopy
Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences.
Single-shot autofocusing of microscopy images using deep learning
We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane.
