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Found 6 papers, 1 papers with code
Divide-Conquer-and-Merge: Memory- and Time-Efficient Holographic Displays
Both simulations and experiments were conducted to demonstrate the capabilities of the proposed framework.
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology.
Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning
There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging.
Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images
To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality.
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution
In particular, we propose to use a convolutional neural network (CNN) to learn the cutoff frequency of real-world degradation process.
Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images
An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma.
Medical Image Segmentation
Retinal OCT Layer Segmentation
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