Image denoising using wavelet thresholding and median filter based Raspberry pi

Abstract

The goal of any de-noising 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 artefacts 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 (σ=10, σ=50, σ=100) and also (σ=15, σ=20, σ=25) to compare with Ramadhan et al.[1] and analysis with it to test embedded system with median filter. Performance evaluation of the median filter, wavelet threshold de-noising techniques is provided. The techniques used are namely the median filter and wavelet threshold is used to remove noise based on raspberry pi with Python. Four methods to remove noise image are used. MF, WT, MF before and after WT. The results showed the image of camera was better than other after tested all the methods with Gaussian noise σ=10. On other hand the other images were better than image of camera for the Gaussian level 50 and 100. The results were good in median filter in wavelet threshold based on Raspberry Pi, which is compared with overall result most of images butter in median filter. KEYWORDSImage Denoising Median Filter Wavelet Threshold Gaussian Noise This is an open-access article under the CC-BY-SA license This research devided in severap part. Section II explain the m methodology includes Literature Review, addition noise model, spatial domain filtering, Wavelet transform, Wavelet threshold, and the parameters. The results willo be delivered in Section III, includes Image Denoising, comparison benchmark. The last section (IV) shows the conclusion.

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