Zahraa F. Hassan; Ibtisam R. Karim; Abdul- Hassan K. Al-Shukur
Abstract
Wavelet analysis has become a powerful tool for denoising images. It represents a new way to achieve better noise reduction and increased contrast. Here, experimentally demonstrate ...
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Wavelet analysis has become a powerful tool for denoising images. It represents a new way to achieve better noise reduction and increased contrast. Here, experimentally demonstrate the abilities of the discrete wavelet transform with Daubechies basis functions for improving the quality of noisy images. In this research, two methods have been compared to modify the coefficients using a soft and hard threshold to improve the visual fineness of noisy images depending on the Root-Mean-Square error (RMS). The low RMS value and better noise reduction are found in the soft threshold methods based on Daubechies wavelet (db8) for the first image RMS=0.101 and the second example RMS=0.109.
