Video surveillance image enhancement using deep learning
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Date
2019-03-01
Authors
Muhamad Faris Che Aminudin
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Abstract
Surveillance camera had become common in improving security because of its usefulness
to capture video and images for analysis. The variation of surveillance camera
model and specification affects the overall image quality. Image quality plays a significant
role in extracting the prominent information from an image. In a face-recognition
system, a bad quality image will affect the performance of the system. Thus, enhancing
the image in image preprocessing before training and testing would deal with
this problem. The low-resolution, low-exposure, and noises are several problems that
occur in surveillance camera. These problems could be addressed by improving the
image resolution and enhancing the contrast and reduce the noise of the image without
overexposing it. In conventional image enhancement, each approach could only solve
one problem at a time and the parameters need to be changed for each problem. This
would cause difficulty in developing an automated system. Therefore, in this research
work, image enhancement using deep learning approach is proposed. Image enhancement
using deep learning utilizes the deep learning network that could automatically
improve the resolution, contrast, and reduce noise of the images without changing any
parameter. To achieve the goal, Deep Learning Image Enhancement (DLIE) is proposed.
There are two deep learning blocks which are Deep Learning Block 1 and Deep
Learning Block2 (DLB1 and DLB2) and image fusion in the proposed DLIE model.
Both DLB1 and DLB2 are proposed to solve their respective problems, which is lowresolution,
low-contrast, and noise. Whereas, image fusion is used as a method to merge DLB1 and DLB2 outputs into one system. DLB1 utilizes convolutional neural network to enhance the low-resolution image using Super Resolution method. Super resolution is one of the algorithms that could improve the image resolution by reconstructing the low-resolution to high-resolution image. On the other hand, DLB2 utilize denoising autoencoder to obtain contrast enhancement and noise reduction before reconstructing the input image to a good quality image. As a result, dark and noise
images can be improved to a cleaner. The outputs of both deep learning techniques
(DLB1 and DLB2) are then fused together using Wavelet image fusion to get the best
image quality while maintaining the capability of both techniques. The enhanced images
are evaluated using image quality assessment such as the peak to signal noise ratio
(PSNR) and structural similarity index (SSIM). DLB1 shows an improvement ranging
from 0.946 to 8 percent, whereas DLB2 shows that it capable of enhancing image
contrast and reduces noise in the image better compared to conventional image enhancement
method. The enhanced image from the DLIE shows improvement in terms
of PSNR compared to the dark and noisy image with minimum average of 13.3625 dB
up to 22.7728 dB, compared to before enhancement which averages of 9.3940 dB up
to 12.8398 dB.