Weighted Nuclear Norm Minimization Image Denoising Method Based on Noise Variance Estimation
Weighted nuclear norm minimization (WNNM) uses image non-local similarity to deal with image denoising; this method not only maintains the detailed texture edge structure but also reduces the impact on distortion of the image after denoising. However, WNNM method assumes that the noise variance of the image is known, where the parameter is set by subjective experience that will result in incompleteness in theory. To handle this issue, it is proposed to pre-estimate noise variance based on discrete wavelet transformation (DWT). The simulation result shows that compared with original WNNM method, pre-estimate noise variance in image denoising has a faster algorithm running speed and a higher image signal-to-noise ratio after denoising.
KeywordsWNNM algorithm Discrete wavelet transformation Singular value decomposition Image denoising
This work was supported by the Fundamental Research Funds for the Central Universities under Grant No. HEUCFP201802.
- 2.Gu S, Zhang L, Zuo W et al. Weighted nuclear norm minimization with application to image denoising. In: IEEE conference on computer vision and pattern recognition, Columbus: IEEE; 2014. p. 2862–9.Google Scholar
- 3.Gu S, Xie Q, Meng D, et al. Weighted nuclear norm minimization and its applications to low level vision. Int J Comput Vis. 2016;121:1–26.Google Scholar
- 4.Canh TN, Dinh KQ, Jeon B. Multi-scale/multi-resolution Kronecker compressive imaging. In: 2015 IEEE international conference on image processing (ICIP), IEEE; 2015. p. 2700–04.Google Scholar