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A Novel and Effective Image Super-Resolution Reconstruction Technique via Fast Global and Local Residual Learning Model

机译：快速全球和局部剩余学习模型的新颖有效图像超分辨率重构技术

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Jingru Hou; Yujuan Si; Xiaoqian Yu;
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1. Super-resolution reconstruction of MR image with a novel residual learning network algorithm [J] . Shi Jun, Liu Qingping, Wang Chaofeng, Physics in medicine and biology. . 2018,第8期

机译：具有新型剩余学习网络算法的MR图像超分辨率重构
2. Super-resolution reconstruction of MR image with a novel residual learning network algorithm [J] . Shi Jun, Liu Qingping, Wang Chaofeng, Physics in medicine and biology. . 2018,第8期

机译：具有新型剩余学习网络算法的MR图像超分辨率重构
3. Optimized highway deep learning network for fast single image super-resolution reconstruction [J] . Ha Viet Khanh, Ren Jinchang, Xu Xinying, Journal of Real-Time Image Processing . 2020,第6期

机译：优化高速公路深度学习网络，用于快速单幅图像超分辨率重建
4. Image Super-Resolution Reconstruction Method Based on Global and Local Residual Learning [C] . Jingru Hou, Yujuan Si, Liangliang Li International Conference on Image, Vision and Computing . 2019

机译：基于全局和局部残差学习的图像超分辨率重建方法
5. Modelling and MMSE reconstruction solutions for image and video super-resolution [D] . Prendergast, Ryan Strong 2008

机译：用于图像和视频超分辨率的建模和MMSE重建解决方案
6. Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction [O] . Daniele Ravì, Agnieszka Barbara Szczotka, Dzhoshkun Ismail Shakir, -1

机译：利用基于视频注册的重建技术对内窥镜单图像超分辨率进行有效的深度学习培训
7. Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction [O] . Daniele Ravì, Agnieszka Barbara Szczotka, Dzhoshkun Ismail Shakir, 2018

机译：基于视频注册视频注册的单图像超分辨率的单图像超分辨率有效深度学习培训
8. Super-Resolution Image Reconstruction by Nonlocal Means Applied to High- Angle Annular Darkfield Scanning Transmission Electron Microscopy (HAADF-STEM). [R] . Binev, P., Blanco-SIlva, F., Blom, D., 2009

机译：非局部均值的超分辨率图像重建应用于高角度环形暗场扫描透射电子显微镜（HaaDF-sTEm）。

A Novel and Effective Image Super-Resolution Reconstruction Technique via Fast Global and Local Residual Learning Model

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