Optical Flow-Guided Multi-Scale Dense Network for Frame Interpolation

2018 ◽  
Author(s):  
Ting Zhang ◽  
Huihui Bai ◽  
Feng Li ◽  
Yao Zhao
Keyword(s):  
Optical Flow ◽  
Dense Network ◽  
Frame Interpolation ◽  
Multi Scale

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 Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1104
Author(s):  
Siti Raihanah Abdani ◽  
Mohd Asyraf Zulkifley ◽  
Nuraisyah Hani Zulkifley
Keyword(s):  
Screening Tool ◽  
Jaccard Index ◽  
Dense Network ◽  
Multi Scale ◽  
Spatial Pyramid Pooling ◽  
Severity Levels ◽  
Set Up ◽  
Channel Variation ◽  
Mobile Phone Camera ◽  
Sunlight Radiation

Pterygium is an eye condition that is prevalent among workers that are frequently exposed to sunlight radiation. However, most of them are not aware of this condition, which motivates many volunteers to set up health awareness booths to give them free health screening. As a result, a screening tool that can be operated on various platforms is needed to support the automated pterygium assessment. One of the crucial functions of this assessment is to extract the infected regions, which directly correlates with the severity levels. Hence, Group-PPM-Net is proposed by integrating a spatial pyramid pooling module (PPM) and group convolution to the deep learning segmentation network. The system uses a standard mobile phone camera input, which is then fed to a modified encoder-decoder convolutional neural network, inspired by a Fully Convolutional Dense Network that consists of a total of 11 dense blocks. A PPM is integrated into the network because of its multi-scale capability, which is useful for multi-scale tissue extraction. The shape of the tissues remains relatively constant, but the size will differ according to the severity levels. Moreover, group and shuffle convolution modules are also integrated at the decoder side of Group-PPM-Net by placing them at the starting layer of each dense block. The addition of these modules allows better correlation among the filters in each group, while the shuffle process increases channel variation that the filters can learn from. The results show that the proposed method obtains mean accuracy, mean intersection over union, Hausdorff distance, and Jaccard index performances of 0.9330, 0.8640, 11.5474, and 0.7966, respectively.

MD-Net: A multi-scale dense network for retinal vessel segmentation

2021 ◽  
Vol 70 ◽  
pp. 102977
Author(s):  
Zhengjin Shi ◽  
Tianyu Wang ◽  
Zheng Huang ◽  
Feng Xie ◽  
Zihong Liu ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Dense Network ◽  
Multi Scale ◽  
Retinal Vessel Segmentation

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
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