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Wavelet analysis for medical image denoising based on thresholding techniques

Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. The term “image or video is de-noising” is usually devoted to the problem connected with AWGN. In this paper, Discrete Wavelet Transform (DWT) is analyzed for medical image denoising. Initially, the AWGN is generated randomly and added to the input medical image. The noisy medical images are decomposed by DWT at various levels. Then, the noises are removed by soft thresholding and hard thresholding the frequency sub-bands of DWT. Results show the denoising performance of DWT based on various thresholding methods.