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Found 10 papers, 4 papers with code
Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations
However, the emotional latent space generated from the existing models is difficult to control the continuous emotional intensity because of the entanglement of features like emotions, speakers, etc.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry.
One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection
The first type learns ROI size-specific cupping artifacts from the analytic reconstruction images, whereas the second type network is to learn to invert the finite Hilbert transform from the truncated differentiated backprojection (DBP) data.
k-Space Deep Learning for Reference-free EPI Ghost Correction
Reconstruction results using 3T and 7T in-vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster. The proposed k-space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high-field MRI without changing the current acquisition protocol.
k-Space Deep Learning for Accelerated MRI
The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion.
Ranked #6 on
Denoising
on Darmstadt Noise Dataset
Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner
For homeland and transportation security applications, 2D X-ray explosive detection system (EDS) have been widely used, but they have limitations in recognizing 3D shape of the hidden objects.
Deep Learning Interior Tomography for Region-of-Interest Reconstruction
Interior tomography for the region-of-interest (ROI) imaging has advantages of using a small detector and reducing X-ray radiation dose.
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose.
Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems
Using numerical experiments with various inverse problems, we demonstrated that our deep convolution framelets network shows consistent improvement over existing deep architectures. This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.
