Search Results for author:
Found 9 papers, 1 papers with code
A GPU-Accelerated Light-field Super-resolution Framework Based on Mixed Noise Model and Weighted Regularization
The experimental results show that our approach achieves better reconstruction quality under severe mixed-noise conditions as compared to the state-of-the-art approaches.
Multi-Material Blind Beam Hardening Correction Based on Non-Linearity Adjustment of Projections
Compared to the state-of-the-art empirical BH correction method, the experiments show that the proposed method can highly reduce the BH artifacts without prior knowledge of the materials.
Computational Scatter Correction for High-Resolution Flat-Panel CT Based on a Fast Monte Carlo Photon Transport Model
Besides, it is shown that for real-world high-resolution flat-panel CT, scatter correction with sufficient accuracy is accomplished within one to three iterations using a FBP and a forward projection computed with the proposed fast MC photon transport model.
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution
Light field (LF) imaging, which captures both spatial and angular information of a scene, is undoubtedly beneficial to numerous applications.
A Resolution Enhancement Plug-in for Deformable Registration of Medical Images
In this work, we consider SR as a preprocessing technique and present a CNN-based resolution enhancement module (REM) which can be easily plugged into the registration network in a cascaded manner.
FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration
Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation.
Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising
In this paper, we propose a regularization technique for noisy-image super-resolution and image denoising.
FDRN: A Fast Deformable Registration Network for Medical Images
Comparing to the auxiliary loss using average Dice score, the proposed multi-label segmentation loss does not induce additional memory cost in the training phase and can be employed on images with arbitrary amount of categories.
Variational Disparity Estimation Framework for Plenoptic Image
This paper presents a computational framework for accurately estimating the disparity map of plenoptic images.
