International Journal of Computer Application Issue 4, Volume 4 (July-August 2014) Available online on http://www.rspublication.com/ijca/ijca_index.htm ISSN: 2250-1797

Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation in order to produce a good quality image. This paper introduces a new technique that is formed by hybridizing the thresholding techniques of the image decomposed using discrete wavelet transform along with the transformation of intensity to obtain a noise free, high quality denoised image. The hybridization of the Bayes and the Neigh Shrink is done to obtain the threshold that provides improved results after the denoising in comparison to when these techniques are used separately. This approach finds its application in denoising of images that are distorted with random Gaussian noise. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of the proposed technique in comparison with the conventional thresholding techniques by the use of parameters mainly, PSNR, MSE and SSIM. The decomposition of the image using the discrete wavelet transform is done iteratively to obtain the best possible results. The most appropriate combination of wavelets is considered for decomposition and reconstruction of the images along with the hybridized thresholding.