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Found 12 papers, 3 papers with code
Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images
Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain respectively.
Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans
In this study, we demonstrate how brain networks can be created from sMRI or PET images and be used in a population graph framework that can combine phenotypic information with imaging features of these brain networks.
A fast and flexible algorithm for microstructure reconstruction combining simulated annealing and deep learning
The proposed algorithm is flexible and can complete training and reconstruction in a short time with only one two-dimensional image.
Multiscale reconstruction of porous media based on multiple dictionaries learning
Digital modeling of the microstructure is important for studying the physical and transport properties of porous media.
Adaptive Consistency Prior Based Deep Network for Image Denoising
To solve this problem, we propose a novel model-based denoising method to inform the design of our denoising network.
Real-World Single Image Super-Resolution: A Brief Review
More specifically, this review covers the critical publically available datasets and assessment metrics for RSISR, and four major categories of RSISR methods, namely the degradation modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR, and self-learning-based RSISR.
FTGAN: A Fully-trained Generative Adversarial Networks for Text to Face Generation
With a novel fully-trained generative network, FTGAN can synthesize higher-quality images and urge the outputs of the FTGAN are more relevant to the input sentences.
Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
To overcome this shortcoming, in this study we proposed a deep learning-based framework for reconstructing full image from its much smaller sub-area(s).
CT-image Super Resolution Using 3D Convolutional Neural Network
Super resolution (SR) methods based on deep learning have achieved surprising performance in two-dimensional (2D) images.
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction.
Ranked #7 on
JPEG Artifact Correction
on LIVE1 (Quality 20 Color)
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
Experiments on compressed images produced by JPEG (we take the JPEG as an example in this paper) demonstrate that the proposed CISRDCNN yields state-of-the-art SR performance on commonly used test images and imagesets.
Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution
This letter presents a novel approach to extract reliable dense and long-range motion trajectories of articulated human in a video sequence.
