Image denoising is the predominant task in the field of image processing and computer vision. The occurrence of noise is due to the influence of various internal and external sources that creates de trop signals, resulting in image noise. In medical images the presence of noise may leads to false clinical diagnosis. To prevent the contingence of noise, various image denoising algorithms are employed expeditiously to uproot the noise. Discrete Wavelet Transform (DWT) is employed to extinguish the occurrence of noise in medical images. It decomposes the input image into detailed and approximate coefficients at three levels. The sampled data is transformed into array of wavelet coefficients. Filters are introduced to eliminate the noises which are coupled with the input image. Wiener Filter, Adaptive Bilateral Filters (ABF) and Boundary Discriminative Noise Detection (BDND) are used to denoise the speckle noise and salt and pepper noise present in the Ultra Sound image. From these results it is observed that ABF filter works well against the speckle noise with the following metrics Peak Signal to Noise Ratio (PSNR), SSIM (Structural Similarity Index Measure), CoC (Coefficient of Correlation), EPI (Edge Preserving Index) for Medical images corrupted with noise.

Wavelet transform, Wiener filter, BDND filter, ABF

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