Total Variation based Multivariate Shearlet Shrinkage for Image Reconstruction

Chengzhi Deng, Saifeng Hu, Wei Tian, Min Hu, Yan Li, Shengqian Wang


Shearlet as a new multidirectional and multiscale transform is optimally efficient in representing images containing edges. In this paper, a total variation based multivariate shearlet adaptive shrinkage is proposed for discontinuity-preserving image denoising. The multivariate adaptive threshold is employed to reduce the noise. Projected total variation diffusion is used to suppress the pseudo-Gibbs and shearlet-like artifacts. Numerical experiments from piecewise-smooth to textured images demonstrate that the proposed method can effectively suppress noise and nonsmooth artifacts caused by shearlet transform. Furthermore, it outperforms several existing techniques in terms of structural similarity (SSIM) index, peak signal-to-noise ratio (PSNR) and visual quality.



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