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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Vydáno v:Neural computing & applications Ročník 34; číslo 4; s. 3079 - 3095
Hlavní autoři: Che Aminudin, Muhamad Faris, Suandi, Shahrel Azmin
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.02.2022
Springer Nature B.V
Témata:
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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Popis
Shrnutí: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, placement and orientation, and the surrounding light. A pixelated, low illumination and noisy image produced by a low-quality surveillance camera causes critical issues for video surveillance face recognition systems. To address these issues, a deep learning image enhancement (DLIE) model is proposed. By utilizing a deep learning architecture such as a convolutional neural network (CNN) and a denoising autoencoder, the image quality can be enhanced. The DLIE model is able to improve image resolution and illumination and reduce noise in an image. There are two deep learning blocks (DLB) in the DLIE model, which are DLB1 and DLB2. Both DLBs are arranged in parallel so that all the stated problems can be addressed simultaneously. DLB1 is proposed to address the occurrence of pixelated images by reconstructing a low-resolution image into a high-resolution image using a CNN. DLB2 used the capability of a denoising autoencoder to reconstruct the corrupted image into a clean image by enhancing the dark and noisy images. The output of each DLB is fused using image fusion to obtain the optimum image quality. The image is evaluated using the peak to signal noise ratio (PSNR) and structural similarity index (SSIM). The enhanced image from the DLIE model exhibits superior quality compared to the original image ranging from 13.3625 to 22.7728 for PSNR and 0.6207 to 0.8155 for SSIM.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-06551-0