Video Super-Resolution With Convolutional Neural Networks

Kappeler, Armin; Yoo, Seunghwan; Dai, Qiqin; Katsaggelos, Aggelos K.

doi:10.1109/tci.2016.2532323

Cited by 598 publications

(448 citation statements)

References 20 publications

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“…This section briefly reviews recently published CNN-based SR techniques [9][10][11]. Although conventional CNN-based SR techniques have been developed for VIS images only, they are meaningful because they can be directly applied to NIR images.…”

Section: Related Workmentioning

confidence: 99%

“…Kappeler et al proposed a CNN that is trained on both the spatial and the temporal dimensions of videos to enhance their spatial resolution [11]. Consecutive frames were motion compensated and they were input to the CNN that provides super-resolved video frames as output.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by VSRnet [11], we have designed a network architecture where both NIR and corresponding VIS images are input as shown in Fig. 3.…”

Section: B Cnn Architecturementioning

confidence: 99%

See 2 more Smart Citations

Convolutional neural network-based infrared image super resolution under low light environment

Han

Kim

Song

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional neural network-based infrared image super resolution under low light environment

Han

Kim

Song

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…In [18], 3D Video SuperResolution Using Fully Convolutional Neural Networks has been proposed to sort out redundancy, degradation in quality of fused image and huge data size problems. In [16], Video SuperResolution with Convolutional Neural Networks adopted to eliminate the problems of video super-resolution. This paper [16] consist problem of ill posed in reconstruction of high dimension super resolution image and training of large datasets is also a vital issue.…”

Section: Video Scaling Issuesmentioning

confidence: 99%

“…In [16], Video SuperResolution with Convolutional Neural Networks adopted to eliminate the problems of video super-resolution. This paper [16] consist problem of ill posed in reconstruction of high dimension super resolution image and training of large datasets is also a vital issue.…”

Section: Video Scaling Issuesmentioning

confidence: 99%

Real-Time Video Scaling Based on Convolution Neural Network Architecture

Safinaz¹,

Kumar²

2017

IJIVP

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In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our

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An Adaptive Automatic Approach to Filtering Empty Images from Camera Traps Using a Deep Learning Model

Yang

Ren

Tan

et al. 2021

Wildlife Society Bulletin

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Camera traps are widely used in wildlife surveys because they are non‐invasive, low‐cost, and highly efficient. Camera traps deployed in the wild often produce large datasets, making it increasingly difficult to manually classify images. Deep learning is a machine learning method that provides a tool to automatically identify images, but it requires labeled training samples and high‐performance servers with multiple Graphics Processing Units (GPUs). However, manually preparing large‐scale training images for training deep learning models is labor intensive, and the high‐performance servers with multiple GPUs are often not available for wildlife management agencies and field researchers. Our study explores an adaptive deep learning method to use small‐scale training sets and a commonly‐available, desktop personal computer (PC) to achieve automatic filtering of empty camera images. Our results showed that by using 29,192 training samples, the overall error, commission error, and omission error of the proposed method on a PC were 2.69%, 6.82%, and 6.45%, respectively. Moreover, the accuracy of our method can be adaptively improved on PCs in actual ecological monitoring projects, which would benefit researchers in field settings when only a PC is available. © 2021 The Wildlife Society.

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Video Super-Resolution With Convolutional Neural Networks

Cited by 598 publications

References 20 publications

Convolutional neural network-based infrared image super resolution under low light environment

Convolutional neural network-based infrared image super resolution under low light environment

Real-Time Video Scaling Based on Convolution Neural Network Architecture

An Adaptive Automatic Approach to Filtering Empty Images from Camera Traps Using a Deep Learning Model

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