Video Surveillance Image Enhancement Using Deep Learning - Aminudin, Muhamad Faris Che (2019)
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.