Frame interpolation of ultrasound images using optical flow

2004 ◽  
Author(s):  
Tae-Jin Nam ◽  
Rae-Hong Park
Keyword(s):  
Optical Flow ◽  
Ultrasound Images ◽  
Frame Interpolation

scholarly journals A Fast 4K Video Frame Interpolation Using a Multi-Scale Optical Flow Reconstruction Network

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1251 ◽  
Author(s):  
Ahn ◽  
Jeong ◽  
Kim ◽  
Kwon ◽  
Yoo
Keyword(s):  
High Resolution ◽  
Optical Flow ◽  
State Of The Art ◽  
Interpolation Method ◽  
Video Frame ◽  
Frame Interpolation ◽  
Multi Scale ◽  
Reconstruction Scheme ◽  
Flow Reconstruction

Recently, video frame interpolation research developed with a convolutional neural network has shown remarkable results. However, these methods demand huge amounts of memory and run time for high-resolution videos, and are unable to process a 4K frame in a single pass. In this paper, we propose a fast 4K video frame interpolation method, based upon a multi-scale optical flow reconstruction scheme. The proposed method predicts low resolution bi-directional optical flow, and reconstructs it into high resolution. We also proposed consistency and multi-scale smoothness loss to enhance the quality of the predicted optical flow. Furthermore, we use adversarial loss to make the interpolated frame more seamless and natural. We demonstrated that the proposed method outperforms the existing state-of-the-art methods in quantitative evaluation, while it runs up to 4.39× faster than those methods for 4K videos.

scholarly journals Frame interpolation in video stream using optical flow methods

2020 ◽  
Vol 1488 ◽  
pp. 012024
Author(s):  
S Lyasheva ◽  
R Rakhmankulov ◽  
M Shleymovich
Keyword(s):  
Optical Flow ◽  
Video Stream ◽  
Frame Interpolation

scholarly journals Automatic Myotendinous Junction Tracking in Ultrasound Images with Phase-Based Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Guang-Quan Zhou ◽  
Yi Zhang ◽  
Ruo-Li Wang ◽  
Ping Zhou ◽  
Yong-Ping Zheng ◽  
...  
Keyword(s):  
Optical Flow ◽  
Automatic Measurement ◽  
Human Motion ◽  
Myotendinous Junction ◽  
Human Motion Analysis ◽  
Ultrasound Images ◽  
Phase Congruency ◽  
Waveform Similarity ◽  
Length Changes

Displacement of the myotendinous junction (MTJ) obtained by ultrasound imaging is crucial to quantify the interactive length changes of muscles and tendons for understanding the mechanics and pathological conditions of the muscle-tendon unit during motion. However, the lack of a reliable automatic measurement method restricts its application in human motion analysis. This paper presents an automated measurement of MTJ displacement using prior knowledge on tendinous tissues and MTJ, precluding the influence of nontendinous components on the estimation of MTJ displacement. It is based on the perception of tendinous features from musculoskeletal ultrasound images using Radon transform and thresholding methods, with information about the symmetric measures obtained from phase congruency. The displacement of MTJ is achieved by tracking manually marked points on tendinous tissues with the Lucas-Kanade optical flow algorithm applied over the segmented MTJ region. The performance of this method was evaluated on ultrasound images of the gastrocnemius obtained from 10 healthy subjects (26.0±2.9 years of age). Waveform similarity between the manual and automatic measurements was assessed by calculating the overall similarity with the coefficient of multiple correlation (CMC). In vivo experiments demonstrated that MTJ tracking with the proposed method (CMC = 0.97±0.02) was more consistent with the manual measurements than existing optical flow tracking methods (CMC = 0.79±0.11). This study demonstrated that the proposed method was robust to the interference of nontendinous components, resulting in a more reliable measurement of MTJ displacement, which may facilitate further research and applications related to the architectural change of muscles and tendons.

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