A New Shearlet Framework for Image Denoising

Wavelets are not very effective in dealing multidimensional signals containing distributed discontinuities such as edges. This paper develops an effective shearlet-based denoising method with a strong ability to localize distributed discontinuities to overcome this limitation. The approach introduced here presents two major contributions: (a) Shearlet Transform is designed to get more directional subbands which helps to capture the anisotropic information of the image; (b) coefficients are divided into low frequency and high frequency subband. Then, the low frequency band is refined by Wiener filter and high-pass bands are denoised via NeighShrink model. The performance of our scheme is measured in terms of peak signal-to-noise ratio (PSNR) and Structural Similarity Index (SSIM). Our results in standard images show the good performance of this algorithm, and prove that the algorithm proposed is robustness to noise, which is not only good for reducing noise, but also has an advantage in holding information of the edge even in high noise level.