Abstract: Image denoising is a process to restore a worth image from degraded image by choosing an appropriate filter but choosing an appropriate filter still is a challenge.Recently, wavelet transform has attracted more and more interest in image de-noising because the wavelet transformof an image produces a non-redundant image representation, which provides better spatial and spectral localization of image formation. The wavelet transform can be interpreted as image decomposition in a set of independent, spatially oriented frequency channels and each cannel can be treated as a single element for removal of noise using an appropriate shrinkage threshold and at the end all treated channels recombined to get denoised image. We discuss in this paper various shrinkage techniques used in wavelet transform for image denoising in respect of test image size, orthogonal wavelets, and elapsed time under Additive White Gaussian Noise.