A Novel Image Denoising Algorithm Based on Non-subsampled Contourlet Transform and Modified NLM
A novel image denoising algorithm based on non-subsampled contourlet transform (NSCT) and modified non-local mean (NLM) is proposed. First, we utilize NSCT to decompose the images to obtain the high frequency coefficients. Second, the high frequency coefficients are used for modified NLM denoising. Finally, the NLM weight values are calculated by modified bisquare function instead of Gaussian kernel function of the traditional NLM, and each noise coefficient is corrected to get the denoised image. According to results of the simulation experiment, the denoising results of the proposed algorithm obtain higher peak signal-to-noise ratio (PSNR) and better retains structural information of image in subjective vision.
KeywordsNon-subsampled contourlet transform (NSCT) Non-local mean (NLM) Denoising
This work was supported in part by National Natural Science Foundation of China (No. 61502356), by Hubei Province Natural Science Foundation of China (No. 2018CFB526).
- 3.Schmidt, U., Gao, Q., Roth, S.: A generative perspective on MRFs in low-level vision. In: Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , San Francisco, CA, pp. 1751–1758 (2010)Google Scholar
- 7.Liu, X.M., Tian, Y., He, H.: Improved non-local means algorithm for image denoising. Comput. Eng. 38(4), 199–207 (2012)Google Scholar
- 11.Goossens, B., Luong, Q., Pizurica, A.: An improved non-local denoising algorithm. In: International Workshop on Local and Non-local Approximation in Image Processing, Switzerland, pp. 143–156 (2008)Google Scholar
- 16.Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), USA, CA, San Diego, pp. 60–65 (2005)Google Scholar
- 17.Goossens, B., Luong, H., Pizurica, A., Philips, W.: An improved non-local denoising algorithm. In: International Workshop on Local Non-local Approximation Image Processing, pp. 143–156 (2008)Google Scholar