Image denoising using orthonormal wavelet transform with stein unbiased risk estimator

De-noising plays a vital role in the field of the image preprocessing. It is often a necessary to be taken, before the image data is analyzed. It attempts to remove whatever noise is present and retains the significant information, regardless of the frequency contents of the signal. It is entirely different content and retains low frequency content. De-noising has to be performed to recover the useful information. In this process much concentration is spent on, how well the edges are preserved and how much of the noise granularity has been removed. In this paper I simulate the different thresholding techniques and compare them their PSNR. After simulation I can find that stein unbiased risk estimator is one of the best techniques for removing the noise from the image in terms of PSNR.