Image Denoising is an important part of diverse image processing and computer vision problems. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. One of the most powerful and perspective approaches in this area is image denoising using discrete wavelet transform (DWT). In this paper, comparison of various Wavelets at different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio (PSNR) of image gets decreased whereas Mean Absolute Error (MAE) and Mean Square Error (MSE) get increased . A comparison of filters and various wavelet based methods has also been carried out to denoise the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other methods.

Image Denoising is an important part of diverse image processing and computer vision problems. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. One of the most powerful and perspective approaches in this area is image denoising using discrete wavelet transform (DWT). In this paper comparative analysis of filters and various wavelet based methods has been carried out. The simulation results show that wavelet based Bayes shrinkage method outperforms other methods in terms of peak signal to noise ratio (PSNR) and mean square error(MSE) and also the comparison of various wavelet families have been discussed in thispaper.