Modified pyramid dual tree direction filter-based image denoising via curvature scale and nonlocal mean multigrade remnant filter

To alleviate the disadvantage of traditional image denoising method in big images data, we propose a modified pyramid dual tree direction filter with nonlocal mean multigrade remnant filter for image denoising in this paper. The proposed denoising method is partitioned into 4 processes. Firstly, curvature scale model is used for building pyramid dual tree direction filter coefficients of noised image. Additionally, the coefficients are calculated by robust Bayes least square method. Then, we use pyramid dual tree direction filter inverse transformation to reconstruct an initial denoised image. At last, nonlocal mean multigrade remnant filter is adopted to filter the initial denoised image and we obtain the final denoised image. The proposed method completely used the multiscale and multidirectional selectivity with approximately translation invariance of pyramid dual tree direction filter. Finally, we assess the image denoising performance of the proposed approach over several test images and compare our results with the state-of-the-art denoising algorithms. Our experiments show that the proposed image denoising method achieves better results than other methods. Furthermore, our new method not only effectively removes the noise but also better keeps the edge and detail information of texture and structure.