Image denoising using wavelet thresholding and median filter based Raspberry pi - Rusul Sabah; Ruzelita Ngadiran; Dalal Abdulmohsin Hammood (Jurnal Informatika, 2021)
The goal of any denoising technique is to remove noise from an image which is the first step in any image processing. The noise removal method should be applied watchful manner. Otherwise, artifacts can be introduced, which may blur the image. In this work, three levels of Gaussian noise are used for adding noise on the original image (Ïƒ=10, Ïƒ=50, Ïƒ =100) and also (Ïƒ=15, Ïƒ=20, Ïƒ=25) to compare with existing work and analysis with it to test embedded system with a median filter. Performance evaluation of the median filter, wavelet threshold denoising techniques is provided. The techniques used are the median filter and wavelet threshold used to remove noise based on raspberry pi with Python. Four methods to remove noise images are used. (Median Filter, Wavelet Thresholding) MF, WT, MF before and after WT. The results showed the camera image was better than the other after tested all the methods with Gaussian noise Ïƒ=10. On the other hand, the other images were better than the camera images for the Gaussian levels 50 and 100. The results were good in the median filter in wavelet threshold based on Raspberry Pi, which is compared with most of the images butter in the median filter.