scholarly journals Fixing Acceleration and Image Resolution Issues of Nuclear Magnetic Resonance

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 681
Author(s):  
Krzysztof Malczewski
Keyword(s):  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Parallel Imaging ◽  
Super Resolution ◽  
Image Resolution ◽  
Body Motion ◽  
Motion Artefacts ◽  
High Resolution Image ◽  
Key Issues ◽  
Resolution Algorithm

Lately, Magnetic Resonance scans have struggled with their own inherent limitations, such as spatial resolution as well as long examination times. A novel, rapid compressively-sensed magnetic resonance high-resolution image resolution algorithm is presented in this research paper. This technique addresses these two key issues by employing a highly-sparse sampling scheme and super-resolution reconstruction (SRR) method. Due to highly challenging requirements for the accuracy of diagnostic images registration, the presented technique exploits image priors, deblurring, parallel imaging, and a deformable human body motion analysis. Clinical trials as well as a phantom-based study have been conducted. It has been proven that the proposed algorithm can enhance image spatial resolution and reduce motion artefacts and scan times.

scholarly journals High-Efficiency Microsatellite-Using Super-Resolution Algorithm Based on the Multi-Modality Super-CMOS Sensor

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4019
Author(s):  
Ke Zhang ◽  
Cankun Yang ◽  
Xiaojuan Li ◽  
Chunping Zhou ◽  
Ruofei Zhong
Keyword(s):  
Spatial Resolution ◽  
High Efficiency ◽  
Super Resolution ◽  
Remote Sensing Images ◽  
High Resolution Image ◽  
Theory To Practice ◽  
Cmos Sensor ◽  
Resolution Algorithm ◽  
Overlap Ratio

To realize the application of super-resolution technology from theory to practice, and to improve microsatellite spatial resolution, we propose a special super-resolution algorithm based on the multi-modality super-CMOS sensor which can adapt to the limited operation capacity of microsatellite computers. First, we designed an oblique sampling mode with the sensor rotated at an angle of 26.56 ∘ ( arctan 1 2 ) to obtain high overlap ratio images with sub-pixel displacement. Secondly, the proposed super-resolution algorithm was applied to reconstruct the final high-resolution image. Because the satellite equipped with this sensor is scheduled to be launched this year, we also designed the simulation mode of conventional sampling and the oblique sampling of the sensor to obtain the comparison and experimental data. Lastly, we evaluated the super-resolution quality of images, the effectiveness, the practicality, and the efficiency of the algorithm. The results of the experiments showed that the satellite-using super-resolution algorithm combined with multi-modality super-CMOS sensor oblique-mode sampling can increase the spatial resolution of an image by about 2 times. The algorithm is simple and highly efficient, and can realize the super-resolution reconstruction of two remote-sensing images within 0.713 s, which has good performance on the microsatellite.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

scholarly journals Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement

2020 ◽  
Vol 12 (5) ◽  
pp. 758 ◽  
Author(s):  
Mengjiao Qin ◽  
Sébastien Mavromatis ◽  
Linshu Hu ◽  
Feng Zhang ◽  
Renyi Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Image Resolution ◽  
Single Image ◽  
Remote Sensing Images ◽  
Practical Applications ◽  
Specific Enhancement ◽  
Urban Monitoring ◽  
High Resolution Images

Super-resolution (SR) is able to improve the spatial resolution of remote sensing images, which is critical for many practical applications such as fine urban monitoring. In this paper, a new single-image SR method, deep gradient-aware network with image-specific enhancement (DGANet-ISE) was proposed to improve the spatial resolution of remote sensing images. First, DGANet was proposed to model the complex relationship between low- and high-resolution images. A new gradient-aware loss was designed in the training phase to preserve more gradient details in super-resolved remote sensing images. Then, the ISE approach was proposed in the testing phase to further improve the SR performance. By using the specific features of each test image, ISE can further boost the generalization capability and adaptability of our method on inexperienced datasets. Finally, three datasets were used to verify the effectiveness of our method. The results indicate that DGANet-ISE outperforms the other 14 methods in the remote sensing image SR, and the cross-database test results demonstrate that our method exhibits satisfactory generalization performance in adapting to new data.

Super Resolution Algorithm for Improving Image Resolution

2015 ◽  
Vol 21 (4) ◽  
pp. 34-42
Author(s):  
Kyoung-Bae Eum ◽  
◽  
Shin-Woo Lee ◽  
Chang-Woo Jeon
Keyword(s):  
Super Resolution ◽  
Image Resolution ◽  
Resolution Algorithm

scholarly journals A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

2020 ◽  
Vol 10 (2) ◽  
pp. 718 ◽  
Author(s):  
K. Lakshminarayanan ◽  
R. Santhana Krishnan ◽  
E. Golden Julie ◽  
Y. Harold Robinson ◽  
Raghvendra Kumar ◽  
...  
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Expectation Maximization ◽  
Super Resolution ◽  
Integrated Approach ◽  
Face Hallucination ◽  
Resolution Image ◽  
High Resolution Image ◽  
Face Images ◽  
Resolution Algorithm

This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.

Study on the Optimal Image Resolution for Image Segmentation

2014 ◽  
Vol 665 ◽  
pp. 724-732
Author(s):  
Yan Tian ◽  
Chong Wu Ruan ◽  
Chen Hong Sui
Keyword(s):  
Image Segmentation ◽  
Super Resolution ◽  
Image Resolution ◽  
Hybrid Index ◽  
Redundant Information ◽  
Image Pyramid ◽  
Image Histogram ◽  
High Resolution Image ◽  
Maximum Value

Resolution is one of the basic and key indexes on assessing the quality of remote sensing image. However, it can not be concluded that the higher the image resolution, the better the segmentation result, since high resolution image contains not only more details of interested object, but also more redundant information of background which causes much difficulty on image segmentation and target recognition. To determine an optimal image resolution for image segmentation, an image pyramid with resolution continuously changing is built by down sampling and super-resolution techniques at first, and then an index called degree of image segmentation is presented based on the image histogram. Degree of image segmentation is a hybrid index which is designed based on integrating the area and symmetry of the valley of the image histogram. At last the optimal image resolution is determined by seeking the maximum value of degree of image segmentation from the images with different resolutions contained in the image pyramid. The experimental results illustrate that degree of image segmentation is directly related with the result of segmentation, and the degree of image segmentation presented in this paper is a good index to describe how well an image can be segmented in the viewpoint of quantitative and qualitative assessing.

scholarly journals Magnetic Resonance Imaging Features under Deep Learning Algorithms in Evaluated Cerebral Protection of Craniotomy Evacuation of Hematoma under Propofol Anesthesia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Deqian Xin ◽  
Zhongzhe An ◽  
Juan Ding ◽  
Zhi Li ◽  
Leyan Qiao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Super Resolution ◽  
Control Group ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Resolution Algorithm ◽  
Experimental Group

This study aimed to explore the value of magnetic resonance imaging (MRI) features based on deep learning super-resolution algorithms in evaluating the value of propofol anesthesia for brain protection of patients undergoing craniotomy evacuation of the hematoma. An optimized super-resolution algorithm was obtained through the multiscale network reconstruction model based on the traditional algorithm. A total of 100 patients undergoing craniotomy evacuation of hematoma were recruited and rolled into sevoflurane control group and propofol experimental group. Both were evaluated using diffusion tensor imaging (DTI) images based on deep learning super-resolution algorithms. The results showed that the fractional anisotropic image (FA) value of the hind limb corticospinal tract of the affected side of the internal capsule of the experimental group after the operation was 0.67 ± 0.28. The National Institute of Health Stroke Scale (NIHSS) score was 6.14 ± 3.29. The oxygen saturation in jugular venous (SjvO2) at T4 and T5 was 61.93 ± 6.58% and 59.38 ± 6.2%, respectively, and cerebral oxygen uptake rate (CO2ER) was 31.12 ± 6.07% and 35.83 ± 7.91%, respectively. The difference in jugular venous oxygen (Da-jvO2) at T3, T4, and T5 was 63.28 ± 10.15 mL/dL, 64.89 ± 13.11 mL/dL, and 66.03 ± 11.78 mL/dL, respectively. The neuron-specific enolase (NSE) and central-nerve-specific protein (S100β) levels at T5 were 53.85 ± 12.31 ng/mL and 7.49 ± 3.16 ng/mL, respectively. In terms of the number of postoperative complications, the patients in the experimental group were better than the control group under sevoflurane anesthesia, and the differences were substantial ( P  < 0.05). In conclusion, MRI images based on deep learning super-resolution algorithm have great clinical value in evaluating the degree of brain injury in patients anesthetized with propofol and the protective effect of propofol on brain nerves.

An Improved Single-Frame Super-Resolution Algorithm for Magnetic Resonance Image

2018 ◽  
Vol 55 (5) ◽  
pp. 051009
Author(s):  
褚晶辉 Chu Jinghui ◽  
胡风硕 Hu Fengshuo ◽  
张佳祺 Zhang Jiaqi ◽  
吕卫 Lü Wei
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Super Resolution ◽  
Single Frame ◽  
Resolution Algorithm
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