WAVELET TRANSFORM BASED IMAGE DENOISING USING DIFFERENT THRESHOLDING METHODS

In the modern age digital images plays important role in the communication purpose. But the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used for different applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. Wavelet transforms enable us to represent signals with a high degree of scarcity. This is the principle behind a non-linear wavelet based signal estimation technique known as wavelet denoising. Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. The idea is to transform the data into the wavelet basis, in which the large coefficients are mainly the signal and the smaller ones represent the noise. By suitably modifying these coefficients, the noise can be removed from the data. The aim of this paper is to study various thresholding techniques such as SureShrink [2], VisuShrink [1] and BayesShrink [3] and compare their performance.