Video surveillance image enhancement via a convolutional neural network and stacked denoising autoencoder

In an extensive-scale surveillance system, the quality of the surveillance camera installed varies. This variation of surveillance camera produces different image quality in terms of resolution, illumination, and noise. The quality of the captured image depends on the surveillance camera hardware, p...

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Vydané v:	Neural computing & applications Ročník 34; číslo 4; s. 3079 - 3095
Hlavní autori:	Che Aminudin, Muhamad Faris, Suandi, Shahrel Azmin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.02.2022 Springer Nature B.V
Predmet:	Artificial Intelligence Artificial neural networks Cameras Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer vision Data Mining and Knowledge Discovery Deep learning Face recognition Illumination Image enhancement Image processing Image Processing and Computer Vision Image quality Image reconstruction Image resolution Machine learning Neural networks Noise Noise reduction Object recognition Original Article Probability and Statistics in Computer Science Signal to noise ratio Surveillance Deep learning Stacked denoising autoencoder Convolutional neural network Video surveillance camera Super resolution Image enhancement Image fusion
ISSN:	0941-0643, 1433-3058
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