Abstract:
This work deals with the white gaussian noise suppression in gray scale images.
Particularly, we have presented the bilateral filtering denoising methods. For the sake of
clarity, we have created some functions in Matlab tools to illustrate the filtered images. Then,
we have studied the wavelet denoising algorithm. We have been interested on the Stein’s
unbiased risk estimate as a linear expansion of thresholds (SURE LET). We have presented a
set of gray scale denoised images by using the Surelet algorithm for various noise levels. In
order to enhance the quality of denoised images, we propose a hybrid algorithm which
combines the Surelet and the bilateral filtering procedures. The first step of this algorithm is
to denoise the input image using Surelet. We develop an efficient joint bilateral filter by using
the wavelet denoising result rather than directly processing the noisy image in the spatial
domain. This filter could suppress the noise while preserve image details. We compare our
denoising algorithm with other denoising techniques in terms of PSNR and visual quality. The
experimental results indicate that our algorithm is competitive with other denoising
techniques.
