In medical image processing, noise plays a major role of reducing the details of the images and blurs the features which are important for the diagnosis of the disease. Retinal images are high resolution images. Gaussian noise, Salt & Pepper noise affects certain important features of them which are needed for proper diagnosis of the retinal diseases. So removing noises in these images is a kind of difficulty that all researcher experiences. The main objective is to reduce the effect of Gaussian noise and Salt & Pepper noise in retinal images. Recently, wavelet transform is being used in all areas of image processing and especially in medical image denoising, it is highly used because it has multi resolution property and sparsity. So here, wavelet thresholding techniques such as Visu shrink, SURE Shrink and Bayes shrink are implemented to remove Gaussian noise and Salt & Pepper noise. The performance of all the three methods are evaluated using PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), NK (Normalized cross correlation) and Time elapsed. Also, the performances of all the thresholding techniques were compared using above mentioned metrics.