Super-Resolution via Matching from Self-Decomposed Codebook with Local Distance Measure Incorporating Pixel Correlation

Author(s):  
Hideaki Kawano ◽  
Noriaki Suetake ◽  
Byungki Cha ◽  
Takashi Aso
2012 ◽  
Vol 39 (11) ◽  
pp. 6779-6790 ◽  
Author(s):  
Hak Soo Kim ◽  
Samuel B. Park ◽  
Simon S. Lo ◽  
James I. Monroe ◽  
Jason W. Sohn

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Taeyong Park ◽  
Kyoyeong Koo ◽  
Juneseuk Shin ◽  
Jeongjin Lee ◽  
Kyung Won Kim

In this paper, we propose a rapid rigid registration method for the fusion visualization of intraoperative 2D X-ray angiogram (XA) and preoperative 3D computed tomography angiography (CTA) images. First, we perform the cardiac cycle alignment of a patient’s 2D XA and 3D CTA images obtained from a different apparatus. Subsequently, we perform the initial registration through alignment of the registration space and optimal boundary box. Finally, the two images are registered where the distance between two vascular structures is minimized by using the local distance map, selective distance measure, and optimization of transformation function. To improve the accuracy and robustness of the registration process, the normalized importance value based on the anatomical information of the coronary arteries is utilized. The experimental results showed fast, robust, and accurate registration using 10 cases, each of the left coronary artery (LCA) and right coronary artery (RCA). Our method can be used as a computer-aided technology for percutaneous coronary intervention (PCI). Our method can be applied to the study of other types of vessels.


Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 54
Author(s):  
Min Zhang ◽  
Huibin Wang ◽  
Zhen Zhang ◽  
Zhe Chen ◽  
Jie Shen

Recently, with the development of convolutional neural networks, single-image super-resolution (SISR) has achieved better performance. However, the practical application of image super-resolution is limited by a large number of parameters and calculations. In this work, we present a lightweight multi-scale asymmetric attention network (MAAN), which consists of a coarse-grained feature block (CFB), fine-grained feature blocks (FFBs), and a reconstruction block (RB). MAAN adopts multiple paths to facilitate information flow and accomplish a better balance of performance and parameters. Specifically, the FFB applies a multi-scale attention residual block (MARB) to capture richer features by exploiting the pixel-to-pixel correlation feature. The asymmetric multi-weights attention blocks (AMABs) in MARB are designed to obtain the attention maps for improving SISR efficiency and readiness. Extensive experimental results show that our method has comparable performance with fewer parameters than the current advanced lightweight SISR.


2021 ◽  
Author(s):  
Qiu Huang ◽  
Yuxin Zhang ◽  
Haoji Hu ◽  
Yongdong Zhu ◽  
Zhifeng Zhao

Acta Naturae ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 42-51
Author(s):  
S. S. Ryabichko ◽  
◽  
A. N. Ibragimov ◽  
L. A. Lebedeva ◽  
E. N. Kozlov ◽  
...  

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