Denoising of Natural Images using Modified VISUShrink on Shearlet Transform

Image denoising is a challenging task as some information contents like that of edges are generally lost in this process. Wavelet transform is used to denoise a 1-D signal, as it can represent point singularity very well. Thereby, it can retain the sudden changes in the signal, even after removing the noise. But an image is a 2-D signal and singularities in it can occur along a curve. Wavelet transform is not a good tool to handle this situation. Shearlet transform can be used instead of wavelet transform in image denoising. There are many thresholding algorithms for the wavelet transform. One such is VISUShrink, a universal thresholding algorithm. This paper proposes a universal thresholding algorithm for Shearlet transform and compares its performance with that of VISUShrink on symmlet4 and daubechies4 wavelets. The results show that the proposed algorithm performs better in most cases. A maximum of 24% increase in PSNR and 27% increase in SSIM are observed.