Flow of wavelet threshold denoising.

<div><p>In the field of image processing, common noise types include Gaussian noise, salt and pepper noise, speckle noise, uniform noise and pulse noise. Different types of noise require different denoising algorithms and techniques to maintain image quality and fidelity. Traditional ima...

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Bibliographic Details
Main Author:	Bingbing Li (461702) (author)
Other Authors:	Yao Cong (2552863) (author), Hongwei Mo (749819) (author)
Published:	2024
Subjects:	Biophysics Cell Biology Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified pseudo gibbs effect better system stability improved threshold function soft threshold algorithm minimum denoising time research method increased hard threshold algorithm maintain image quality digital image denoising image denoising technology remove image noise improve image quality research method image denoising hard ta continuous function reconstructed image output image image processing uniform noise speckle noise pulse noise pepper noise noise ratio noise information gaussian noise xlink "> wavelet transform true signal smaller errors saved 9ms results showed results indicate positive direction positive development peak signal lena images first combined effectively promote different types different densities different algorithms detail loss clean removal cannot guarantee also result aforementioned issues 89 %. 3ms compared
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Flow of wavelet threshold denoising.

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