A Comparative Study of Image Denoising in IoT
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیایی
1 - IT Department,Esfahan Water and Waste Water Company,Esfahan,Iran
کلید واژه: Noise, Image denoising, IoT, Wiener filter, Deep neural network,
چکیده مقاله :
During capturing and transmitting images in IoT devices, recorder media have some physical constraints that make them prone to noise. Noise shows itself in the form of signal disturbances that result in the inhibition in observation, analysis, and evaluation of the image. Various image denoising algorithms have been proposed so far in order to remove noise from the degraded image. In particular, application of soft computing methods such as utilizing deep neural networks on digital images as an approach with better results has recently been considered. With regard to the importance of accurate and noise-free storage of images taken from surveillance cameras in spatial systems of water and wastewater industry, this paper describes some key concepts. After that, an additive noise is utilized on the original image captured by surveillance cameras. Then, two approaches, the Wiener filter and a deep neural network method called DnCNN are being compared for the denoising purpose. Results showed that the deep neural network led to better performance than the Wiener filter in terms of PSNR measure.
During capturing and transmitting images in IoT devices, recorder media have some physical constraints that make them prone to noise. Noise shows itself in the form of signal disturbances that result in the inhibition in observation, analysis, and evaluation of the image. Various image denoising algorithms have been proposed so far in order to remove noise from the degraded image. In particular, application of soft computing methods such as utilizing deep neural networks on digital images as an approach with better results has recently been considered. With regard to the importance of accurate and noise-free storage of images taken from surveillance cameras in spatial systems of water and wastewater industry, this paper describes some key concepts. After that, an additive noise is utilized on the original image captured by surveillance cameras. Then, two approaches, the Wiener filter and a deep neural network method called DnCNN are being compared for the denoising purpose. Results showed that the deep neural network led to better performance than the Wiener filter in terms of PSNR measure.
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