High-resolution video inpainting based on spatial structure and temporal edge information

2020 ◽  
Author(s):  
Dezhi Bo ◽  
Ran Ma ◽  
Keke Wang ◽  
Kai Li ◽  
Ping An
Keyword(s):  
High Resolution ◽  
Spatial Structure ◽  
Edge Information ◽  
Video Inpainting

Observations of the spatial structure of interstellar hydrogen

1967 ◽  
Vol 31 ◽  
pp. 45-46
Author(s):  
Carl Heiles
Keyword(s):  
High Resolution ◽  
Spatial Structure ◽  
Velocity Dispersion ◽  
Temperature Variations

High-resolution 21-cm line observations in a region aroundlII= 120°,b11= +15°, have revealed four types of structure in the interstellar hydrogen: a smooth background, large sheets of density 2 atoms cm-3, clouds occurring mostly in groups, and ‘Cloudlets’ of a few solar masses and a few parsecs in size; the velocity dispersion in the Cloudlets is only 1 km/sec. Strong temperature variations in the gas are in evidence.

scholarly journals High resolution infrared maps of IRAS galaxies

1991 ◽  
Vol 147 ◽  
pp. 411-412
Author(s):  
S. K. Ghosh ◽  
T. N. Rengarajan ◽  
R. P. Verma
Keyword(s):  
High Resolution ◽  
Spatial Structure ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
The Cross

IRAS has provided infrared maps of a large number of galaxies. However the size of IRAS survey detectors is quite large (∼5′ in the cross-scan direction) and there is a need for improving the resolution of these maps. In order to understand the spatial structure of galaxies, we have generated high resolution maps of 18 large galaxies by deconvolving the IRAS pointed observations from survey detectors as well as from Chopped Photometric Channel (CPC) using Maximum Entropy Method (MEM).

scholarly journals Fuzzy Control Method for Synchronous Acquisition of High Resolution Image based on Machine Learning

2022 ◽  
Vol 16 ◽  
pp. 367-373
Author(s):  
Zichong Chen ◽  
Xianwen Luo
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Fuzzy Control ◽  
Control Method ◽  
Learning Algorithm ◽  
Fuzzy Membership Function ◽  
Edge Information ◽  
Resolution Image ◽  
High Resolution Image ◽  
Fuzzy Space

Aiming at the problem of low baud rate of traditional high-resolution image synchronous acquisition fuzzy control method, a high-resolution image synchronous acquisition fuzzy control method based on machine learning is designed. By detecting the fuzzy edge information of high-resolution image, the fuzzy membership function of synchronous acquisition quantity is proposed, and the gradient amplitude of synchronous acquisition quantity of high-resolution image is calculated. The unsupervised learning algorithm based on machine learning is used to cluster the fuzzy control data, so as to determine the fuzzy space of synchronous acquisition quantity of high-resolution image, and calculate the fuzzy feature similarity, the fuzzy control of synchronous acquisition quantity of high resolution image is realized. Experimental results show that the controlled wave rate in this paper solves the problem of low wave rate in 255.63 bps/h-271.33 bps/h, and significantly improves the control accuracy.

scholarly journals High resolution infrared maps of IRAS galaxies

1991 ◽  
Vol 147 ◽  
pp. 411-412
Author(s):  
S. K. Ghosh ◽  
T. N. Rengarajan ◽  
R. P. Verma
Keyword(s):  
High Resolution ◽  
Spatial Structure ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
The Cross

IRAS has provided infrared maps of a large number of galaxies. However the size of IRAS survey detectors is quite large (∼5′ in the cross-scan direction) and there is a need for improving the resolution of these maps. In order to understand the spatial structure of galaxies, we have generated high resolution maps of 18 large galaxies by deconvolving the IRAS pointed observations from survey detectors as well as from Chopped Photometric Channel (CPC) using Maximum Entropy Method (MEM).

scholarly journals High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qiao Ke ◽  
Sun Zeng-guo ◽  
Yang Liu ◽  
Wei Wei ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Iterative Algorithm ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Edge Information ◽  
Nonlocal Means ◽  
Proposed Model ◽  
Effective Performance

A new speckle suppression algorithm is proposed for high-resolution synthetic aperture radar (SAR) images. It is based on the nonlocal means (NLM) filter and the modified Aubert and Aujol (AA) model. This method takes the nonlocal Dirichlet function as a linear regularization item, which constructs the weight by measuring the similarity of images. Then, a new despeckling model is introduced by combining the regularization item and the data item of the AA model, and an iterative algorithm is proposed to solve the new model. The experiments show that, compared with the AA model, the proposed model has more effective performance in suppressing speckle; namely, ENL and DCV measures are 21.75% and 4.5% higher, respectively, than for NLM. Moreover, it also has better performance in keeping the edge information.

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