scholarly journals Model‐based super‐resolution reconstruction of T 2 maps

2019 ◽  
Vol 83 (3) ◽  
pp. 906-919 ◽  
Author(s):  
Wajiha Bano ◽  
Gian Franco Piredda ◽  
Mike Davies ◽  
Ian Marshall ◽  
Mohammad Golbabaee ◽  
...  
Keyword(s):  
Super Resolution ◽  
Model Based

Robust Face Super-Resolution via Position Relation Model Based on Global Face Context

2020 ◽  
Vol 29 ◽  
pp. 9002-9016
Author(s):  
Liang Chen ◽  
Jinshan Pan ◽  
Junjun Jiang ◽  
Jiawei Zhang ◽  
Yi Wu
Keyword(s):  
Super Resolution ◽  
Model Based ◽  
Relation Model

Model-Based Super-Resolution of Diffusion MRI

2013 ◽  
pp. 25-34 ◽  
Author(s):  
Alexandra Tobisch ◽  
Peter F. Neher ◽  
Matthew C. Rowe ◽  
Klaus H. Maier-Hein ◽  
Hui Zhang
Keyword(s):  
Diffusion Mri ◽  
Super Resolution ◽  
Model Based

Super-resolution reconstruction and higher-degree function deformation model based matching for Chang’E-1 lunar images

2009 ◽  
Vol 52 (12) ◽  
pp. 3468-3476 ◽  
Author(s):  
LiChun Li ◽  
QiFeng Yu ◽  
Yun Yuan ◽  
Yang Shang ◽  
HongWei Lu ◽  
...  
Keyword(s):  
Super Resolution ◽  
Deformation Model ◽  
Model Based ◽  
Degree Function

A super resolution frontal face generation model based on 3DDFA and CBAM

Displays ◽  
2021 ◽  
pp. 102043
Author(s):  
Chuanming Niu ◽  
Fangzhe Nan ◽  
Xinran Wang
Keyword(s):  
Super Resolution ◽  
Generation Model ◽  
Model Based ◽  
Face Generation

scholarly journals Generative and discriminative model-based approaches to microscopic image restoration and segmentation

Microscopy ◽  
2020 ◽  
Vol 69 (2) ◽  
pp. 79-91 ◽  
Author(s):  
Shin Ishii ◽  
Sehyung Lee ◽  
Hidetoshi Urakubo ◽  
Hideaki Kume ◽  
Haruo Kasai
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Restoration ◽  
Object Segmentation ◽  
Super Resolution ◽  
Data Driven ◽  
Microscopic Image ◽  
Microscopic Images ◽  
Model Based ◽  
Discriminative Model

Abstract Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.

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