A geometrical wavelet shrinkage approach for image denoising

In this paper a denoising technique for digital gray value images corrupted with additive Gaussian noise is presented. We studied a recently proposed hard thresholding technique which uses a two stage selection procedure in which coefficients are selected based on their magnitude, spatial connectedness and interscale dependencies. We construct a shrinkage version of the algorithm which outperforms the original one. We also present a new hard thresholding algorithm which incorporates the spatial connectivity information in a more simple and efficient way and construct a shrinkage version of it. The new algorithms are faster and lead to better denoising performances compared to the original one, both visually and quantitatively.