The challenge of image denoising is how to filter the noise as more as possible while preserve the image information at the same time. To reduce this contradiction

we propose an iterative image denoising method by combining the non-local techniques and the high order singular value decomposition (HOSVD). Our method first uses the non-local patch clustering and the HOSVD to construct the data-adaptive 3D transform basis and coefficients. Then an inverse transformation is taken to get the denoised patches after filtering the coefficients by a threshold value. Usually

denoising once is not enough to get the desired denoised result. Therefore

we design an iterative denoising strategy for the proposed method

which is verified to be a process to take the trade-off between bias and variance and optimize the mean square error (MSE). Experiments illustrate that our method not only filter the noise effectively but also preserve the texture well. Our method combines advantages of both non-local collaborative filtering and data-adaptive denoising. Compared with the other three advanced denoising methods

such as

BM3D

NCSR and PLOW

our method outperforms them in terms of texture preserving.