A new motion estimation method for motion-compensated frame interpolation using a convolutional neural network

2017 ◽  
Author(s):  
Giyong Choi ◽  
PyeongGang Heo ◽  
Se Ri Oh ◽  
HyunWook Park
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Convolutional Neural Network ◽  
Estimation Method ◽  
Frame Interpolation ◽  
Motion Compensated Frame Interpolation

scholarly journals Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model

2019 ◽  
Vol 82 (4) ◽  
pp. 1452-1461 ◽  
Author(s):  
Melissa W. Haskell ◽  
Stephen F. Cauley ◽  
Berkin Bilgic ◽  
Julian Hossbach ◽  
Daniel N. Splitthoff ◽  
...  
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Convolutional Neural Network ◽  
Motion Correction ◽  
Motion Model ◽  
Accelerated Motion

scholarly journals Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

A Bayer motion estimation for motion-compensated frame interpolation

2019 ◽  
Vol 78 (14) ◽  
pp. 19603-19619
Author(s):  
Ran Li ◽  
Bingyu Ji ◽  
Yanling Li ◽  
Changan Wu
Keyword(s):  
Motion Estimation ◽  
Frame Interpolation ◽  
Motion Compensated Frame Interpolation

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.

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