Convolutional neural network with coarse-to-fine resolution fusion and residual learning structures for cross-modality image synthesis

2022 ◽  
Vol 71 ◽  
pp. 103199
Author(s):  
Guoqing Wu ◽  
Xi Chen ◽  
Zhifeng Shi ◽  
Dachuan Zhang ◽  
Zhaoyu Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Synthesis ◽  
Residual Learning ◽  
Fine Resolution ◽  
Coarse To Fine

MCFF-CNN: Multiscale comprehensive feature fusion convolutional neural network for vehicle color recognition based on residual learning

2020 ◽  
Vol 395 ◽  
pp. 178-187 ◽  
Author(s):  
Huiyuan Fu ◽  
Huadong Ma ◽  
Gaoya Wang ◽  
Xiaomou Zhang ◽  
Yifan Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Color Recognition ◽  
Residual Learning

Residual Learning Based Convolutional Neural Network for Super Resolution

2019 ◽  
Vol 7 (4) ◽  
pp. 126-129
Author(s):  
Hwei Jen Lin ◽  
◽  
Yoshimasa Tokuyama ◽  
Zi Jun Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Residual Learning

scholarly journals Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network

2018 ◽  
Vol 36 (9) ◽  
pp. 566-574 ◽  
Author(s):  
Dongsheng Jiang ◽  
Weiqiang Dou ◽  
Luc Vosters ◽  
Xiayu Xu ◽  
Yue Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Magnetic Resonance Images ◽  
Residual Learning

scholarly journals A Fast 4K Video Frame Interpolation Using a Hybrid Task-Based Convolutional Neural Network

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 619 ◽  
Author(s):  
Ha-Eun Ahn ◽  
Jinwoo Jeong ◽  
Je Woo Kim
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
State Of The Art ◽  
Visual Quality ◽  
Video Frame ◽  
Frame Interpolation ◽  
Algorithm Efficiency ◽  
Coarse To Fine

Visual quality and algorithm efficiency are two main interests in video frame interpolation. We propose a hybrid task-based convolutional neural network for fast and accurate frame interpolation of 4K videos. The proposed method synthesizes low-resolution frames, then reconstructs high-resolution frames in a coarse-to-fine fashion. We also propose edge loss, to preserve high-frequency information and make the synthesized frames look sharper. Experimental results show that the proposed method achieves state-of-the-art performance and performs 2.69x faster than the existing methods that are operable for 4K videos, while maintaining comparable visual and quantitative quality.

scholarly journals A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 607 ◽  
Author(s):  
Jianwei Lu ◽  
Yixuan Xu ◽  
Mingle Chen ◽  
Ye Luo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Original Data ◽  
Vessel Segmentation ◽  
Retinal Diseases ◽  
Fundus Images ◽  
The Neural Network ◽  
Coarse To Fine

Fundus vessel analysis is a significant tool for evaluating the development of retinal diseases such as diabetic retinopathy and hypertension in clinical practice. Hence, automatic fundus vessel segmentation is essential and valuable for medical diagnosis in ophthalmopathy and will allow identification and extraction of relevant symmetric and asymmetric patterns. Further, due to the uniqueness of fundus vessel, it can be applied in the field of biometric identification. In this paper, we remold fundus vessel segmentation as a task of pixel-wise classification task, and propose a novel coarse-to-fine fully convolutional neural network (CF-FCN) to extract vessels from fundus images. Our CF-FCN is aimed at making full use of the original data information and making up for the coarse output of the neural network by harnessing the space relationship between pixels in fundus images. Accompanying with necessary pre-processing and post-processing operations, the efficacy and efficiency of our CF-FCN is corroborated through our experiments on DRIVE, STARE, HRF and CHASE DB1 datasets. It achieves sensitivity of 0.7941, specificity of 0.9870, accuracy of 0.9634 and Area Under Receiver Operating Characteristic Curve (AUC) of 0.9787 on DRIVE datasets, which surpasses the state-of-the-art approaches.

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